This PDF file contains the front matter associated with SPIE Proceedings Volume 13157, including the Title Page, Copyright information, Table of Contents, and Conference Committee information.

In the aerial photogrammetry, the position and attitude information of the aerial camera at the time of exposure can be obtained through the positioning and orientation system carried on board, which is introduced into the collinear equation to solve the geodetic coordinates of the target points, and the direct geographic positioning can be completed and positioning the lever arm of directional system will bring the output error, resulting in collinear equation of deviating from the collinear condition, thus affecting the precision of the target for the quantitative analysis of the lever arm effects on the accuracy of positioning and orientation system, this paper analyzes the inertial measurement unit under different installation method of the lever arm type, use the data analysis of two kinds of semi-physical simulation installation effect the precision of the system, in when the pendulum is swept at 60 degrees, the attitude error is about 0.01. According to the results, the error compensation model of the lever arm is established, which provides a theoretical basis for compensating the influence of the lever arm error.

Based on the demand of working environments below liquid nitrogen temperature for very long wave infrared detectors, this article proposes a kind of packaging technology for deep low-temperature working very long wave infrared detectors. Through innovative optimization design of the heat leakage and the chip electrical lead structure of the dewar, the static thermal load of the entire dewar can be controlled to be 0.65W when the chip is operating at a low temperature of 30K, and the static thermal load at the coldest end position is 0.3W, The cooling capacity of the two-stage pulse tube refrigerator that is compatible with the dewar can meet the above thermal load requirement. We have completed the packaging test of the detector component, and the results show that under the air cooling test condition of the refrigerator expander’s hot end, the detector chip can work in the temperature of 30K; the outer contour of the dewar is < Φ 130mmx180mm. This technological achievement greatly promotes the advancement of packaging technology for very long wave array infrared detectors operating at deep and low temperature.

Drop point measurement precision is one of the core indexes to evaluate the combat effectiveness of weapons. With the development of experimental equipment, the experimental training venue has been expanded to the far sea. Due to the little known data in the far sea area and the measuring area, the measurement methods are limited. In response to the characteristics of the high seas, this paper proposes a method of mounts an optoelectronic pod on a drone and utilizes two drones for collaborative intersection measurement, achieving high-precision landing point measurement and high reliable data acquisition rate. This paper provides a detailed comparison between the traditional H-E-A single station angle measurement and distance measurement methods, the collinear equation based non ranging information positioning method, and the dual aircraft intersection positioning measurement principle combined with RLS filtering algorithm. At the same time, this paper analyzed various factors that affect the accuracy of positioning measurement. Through actual measurement verification of simulated targets, this method achieved a drop point measurement accuracy of 2m within a range of 3Km and a measurement accuracy of over 95%, which is significantly improved compared to traditional methods. The method provides data support for evaluating weapon effectiveness and obtaining field situation, and can also serve as auxiliary means for personnel search and rescue, debris search, etc., greatly improving the fusion ability of multidimensional data and enhancing the independent innovation and support ability of far sea measurement equipment.

In recent years, onion-like carbons (OLCs), as a new type of carbon nanostructure, have emerged as significant in biomedical fields. OLCs are capable of being internalized by cells, interacting with various organelles, and thereby influencing cellular physiological processes. Consequently, there is considerable interest in the rapid, non-invasive detection and statistical analysis of intracellular OLCs. Holographic flow cytometry provides a high-throughput, label-free imaging approach, introducing a new approach for detecting intracellular OLCs. Indeed, cells can act as biological lenses, and the presence of intracellular OLCs alters their refractive index distribution, affecting their optical focusing and lensing features. In this study, we merged the 3D refractive index distribution of cells, obtained through in-flow tomographic experiments, with appropriate numerical simulations. This combination demonstrates that intracellular OLCs can be effectively detected by analyzing 2D quantitative phase maps, without the need for additional manual labeling. The experiments were conducted on colon cancer cells, both with and without intracellular OLCs. The results indicate that the biolens properties of cells can serve as a valuable biomarker for detecting intracellular OLCs. This promotes the research on OLCs-related physiological processes using holographic flow cytometry, enabling high-throughput, non-invasive screening of statistically significant number of cells.

Satellite remote sensing observations and on-site observations on offshore platforms are currently the two most mainstream means of observing characteristics of marine aerosols. Among them, satellite remote sensing observations have the advantage of a wide range of observations, while on-site observations on offshore platforms can obtain observation data with higher time resolution, and have the advantages of easy calibration and maintenance, and high-quality observation data. By using the measured aerosol optical thickness data from the Calibration Validation Field Network of Autonomous Ocean Satellites in the North Yellow Sea, the main aerosol types and optical properties of the offshore station are discussed to provide theoretical and technical support for the calibration and validation of autonomous ocean satellites, as well as the commercial operation of autonomous calibration and validation field networks. Using data from January 2020-June 2023, the data were classified into six main aerosol types according to the graphical classification: clean, desert dust, continental, subcontinental, urban industrial and biomass burning aerosols, and the main aerosol particles at the stations were analyzed by the ESI, single scattering albedo and fine mode fraction. The results show that spring is the most polluted season among the four seasons in the area, continental-type aerosols dominate throughout the year, and the main aerosol type at the station is a continental type aerosol with weak and moderate absorption coarse particle mode.

Unlike traditional optical cameras, Event Cameras are a new type of neuromorphic vision sensors which generate asynchronous streams of events in response to changes in log-illumination at each pixel. These devices are, therefore, extremely fast, allow for imaging while the device is moving, and enable low-power space imaging equally well during daytime as well as night. It can compensate for the limitations of optical equipment detection and meet the current space object detection requirements. Based on the background of space object detection and the optical observation technology of event cameras, the research status and several future development trends of event-based space object detection methods are summarized and reviewed. Firstly, the basic principle of event cameras and the advantages and disadvantages of their application in the aerospace field are described. Then the technical development of event cameras in space object detection, recognition and tracking are introduced. Finally, the development direction of event cameras in space object detection is discussed.

With the maturity of satellite R&D technology and the reduction of costs, a large number of high-resolution optical commercial satellites have been launched, and more and more optical satellite constellations will be gradually built and applied, which will gradually improve the ability of satellite to observe the earth, and shorten the revisiting cycle of the same area. Using the rapid revisiting and high-resolution imaging capabilities of satellite constellations, sub-meter optical images and digital elevation information of target areas can be quickly extracted. This paper proposes a method for evaluating the road trafficability of target areas using high-resolution satellite images and digital elevation models. Firstly, the optical texture of satellite images can be used to analyze and obtain the attribute information such as the basic types of road pavement. At the same time, using the high-resolution features of satellite images, the road outline can be identified through image segmentation technology, and then the geometric characteristics such as road width and connectivity can be obtained. After that, the digital elevation model of the target area is accurately matched with the satellite image to extract the road elevation information, and obtain the geometric characteristics such as road slope. Finally, the extracted road geometric attribute information is used to evaluate and analyze the trafficability between roads. The evaluation results can be used to predict the trafficability and time of vehicles, and also to scientifically plan the traffic route of vehicles.

Ocean bathymetric LiDAR relies on high efficiency, high precision, fast, safe, etc. characteristics, can used to conveniently acquire underwater terrain data, ocean surface laser scattering, near-coastal engineering, and detection of underwater mines and other targets. So, the United States, Canada, Australia, etc., have competed to develop ocean bathymetric LiDAR. Laser energy, laser repetition frequency, max sounding depth, min sounding depth, point cloud density and vertical accuracy have been greatly improved. Because the problems of large size, heavy weight, and the need to carry manned aircraft platforms and airport runways of this LiDAR, the United States, Austria and China have begun to study single-frequency LiDAR that are small in size, light in weight, capable of carrying UAV platforms, and take off anytime, anywhere.

The characteristics of space targets are the basis of detecting and identifying space targets. The size, shape and material of the space target are closely related to its scattering characteristics. Starting with the three typical influencing factors of dimensional accuracy, shape and material, this paper designed a typical simple body and assembly model with 15% dimensional accuracy, different shapes and different materials, and analyzed the influence of dimensional accuracy, shape and material on scattering characteristics according to the calculation results of the model's omnidirectional luminosity. The research results show that, when the size deviation is less than 15%, it has little effect on the scattering characteristics of the target. When the size is equivalent, the influence of different shapes on the scattering characteristics of the target is not significant, and the surface material has the greatest influence on the scattering characteristics of the target.

We report and demonstrate full-Stokes imaging polarimetry via random retarder rotation (R3). The method adds an assistant imaging channel comprising a linear polarization array and point source to the classical full-Stokes imaging optical path. Further, an alternating iterative algorithm of the polarization information matrix is used to solve the rotation angle of the phase retarder to obtain the full-Stokes information of the imaging target. Theoretical analysis, numerical simulations, and comparative experiments are conducted to verify that the proposed method can achieve accurate full-Stokes imaging measurements via R3. To the best of our knowledge, this is the first time that a full-Stokes imaging method with high precision performance has been proposed that does not depend on accuracy of rotation angle.

Animals in nature always incorporate their own color, texture, shape, and other features into the surrounding environment to achieve camouflage while trying to hunt and avoid natural enemies. Humans have also successfully applied this concept to various fields such as military camouflage, wilderness exploration, and endangered animal protection by imitating animal behaviors. However, these behaviors also make it difficult to identify camouflaged objects in images. To address this issue, a camouflage image enhancement algorithm based on HSV and histogram equalization is proposed in this paper. First, convert the camouflage image from RGB to HSV space and afterward process only the V component. Next, calculate the peaks and valleys of the V-component histogram, whereby the histogram is partitioned into several peak regions. Then, histogram equalization and dynamic range regression are performed separately for each region of the image corresponding to the histogram peak. Finally, the processed regions are combined to generate the enhanced image. The proposed algorithm is evaluated using the CAMO dataset, and the results show that our method outperforms similar algorithms in terms of image quality measures such as PIQE, ILNIQE, OG-IQA, and BLIINDS-II, in both the overall image and the camouflaged region. In contrast, for the non-camouflaged region, our method is mostly weaker than other methods in all measures, indicating that our method has a positive enhancement for the camouflaged object and less influence on the background region.

With the rapid development of information society, human requirements for data computing and processing capacity are also constantly improved, not only in the field of national defense and aerospace, oil exploration, scientific research, and government information, finance, education and enterprises need to deal with practical problems through a large number of high-performance computing. This paper designs a cluster scheduling system based on high performance hybrid architecture to solve the problem that traditional high performance computing can only rely on the X86 ecosystem. Through the development of cluster scheduling technology of high performance hybrid architecture, the business software scheduling problem under the condition of "X86+ARM" hybrid architecture is realized. Through a series of tests, it is verified that the hybrid architecture runs well and achieves the expected results.

MODIS data and SEBS model were used to estimate the surface energy flux in Hefei City from March to December 2021. Verified by comparison with the EC measured values, and the sensitivity analysis of each parameter in the model was carried out. The results show that the net radiation flux(Rn) is the highest in September and the lowest in December. The Rn of water body is the highest, and urban area is the lowest. There was little correlation between soil heat flux(G) and seasonal variation. The main urban area and water body were higher, while the G was lower in the area with high vegetation coverage. The sensible heat flux(H) is obviously affected by the seasons, and the average H in December is the lowest, and even negative. Compared with the measured value, the average absolute error is 9W/m², and the average relative error is 7%. The latent heat flux(LE) average absolute error between the inversion value and the measured value is 97W/m², and the average relative error is 25.9%. The LE is relatively small in the main urban area, and relatively large in the area with high vegetation coverage. Sensitivity analysis of the model parameters was shows that the Rn is negatively correlated with the surface reflectance and surface temperature, and the expansion and contraction of air pressure, NDVI and wind speed have no effect on the Rn, G was negatively correlated with NDVI. Surface temperature and air temperature have the greatest influence on H and LE.

Crop lodging is a phenomenon in which plant stems change from a naturally upright state to a permanent dislocation caused by external factors. It is a common disaster in agricultural production and will directly affect the yield and quality of crops. It needs to be discovered and intervened in time. Among the current remote sensing methods, optical remote sensing is limited by the uncertainty of spectral changes, and it is difficult to distinguish lodging from other complex influencing factors such as drought, pests and diseases, and it cannot be observed at night, which is easily affected by clouds and rain. Synthetic aperture radar (SAR) technology has the capability of all-day and all-weather monitoring. However, the methods using SAR in existing research are mostly simple exponential structures and thresholds for extraction, which have poor applicability. Since polarimetric SAR can obtain multi-channel scattering characteristics of crops, this paper proposed a method of crop lodging region detection using polarimetric SAR images. First, the H/A/Alpha polarization decomposition is performed to obtain a series of polarization features, and then the SVM classifier is established by using the polarization features and the backscattering intensity of different polarization modes. Finally, the lodging detection of crops in the area is completed. The experimental results show that the crop lodging area extracted by Polarimetric SAR and Support Vector Machine (SVM) is consistent with the field survey results, and it is a reliable means for monitoring the growth status of large-scale crops.

In modern life, it is necessary to image certain targets under low-light conditions such as dark night or morning dusk, and low-light night vision technology is one of the main technologies to expand the night visual perception of the human eye, and low-light remote sensing camera can greatly expand the effective working time range of various spacecraft, so that it can observe, report and warn ground emergencies in a wider period of time. The FPGA-driven scientific-grade CMOS image sensor realizes the imaging function of the low-light camera, and realizes the real-time digital TDI function and automatic exposure algorithm on the FPGA. The results of the exterior imaging experiment show that the contours of buildings, street lamps, trees and wires near the light source can be distinguished under the condition of low-light illumination, and the imaging effect of the low-light camera reaches the index.

While convolutional neural networks have shown promise in medical image registration, their inherent complexity limits their registration speed, particularly for surgical applications. Additionally, traditional feature-based matching methods struggle with multi-modal forearm image registration due to the simplicity of forearm skin textures. To address these issues, we propose a robust forearm feature point extraction method based on the forearm’s structural invariance. We combine this method with thin plate spline interpolation to achieve multi-modal forearm registration. Our approach introduces the Forearm Feature Representation Curve (FFRC) and the Multi-Modal Image Registration Framework (FAM) for aligning forearm images with digital anatomical models. FFRC identifies feature points based on forearm structural characteristics, and FAM employs FFRC for matching point pre-screening before applying an affine transformation. For deformable registration which adds Thin Plate Spline (FAM-TPS) uses the matched points as control points. In our experiments, both FAM and FAM-TPS demonstrate high registration accuracy, with FAM-TPS outperforming conventional feature-based methods. Our framework excels at registering forearm images with varying rotation angles, and we have observed a strong correlation between the feature curve’s peak value and the rotation angle. These results affirm the effectiveness of our approach in achieving precise and resilient registration.

Laser communication technology has garnered considerable attention in recent times due to its advantageous features such as high security, wide bandwidth, and high transmission rate. Its potential for long-distance atmosphere-sea information transmission is particularly promising. In this study, we developed a laser communication system that utilized a high-altitude aircraft and an underwater platform, enabling communication distances of up to 10 km in the air and 100 m underwater. The system incorporates a high-power blue-green laser, delivering an energy of 80 mJ per laser pulse with a repetition frequency of 100 Hz. During experimental investigations, we found that the Doppler shift effect becomes more pronounced with communication time. This phenomenon becomes more pronounced when transmitting large-capacity data, as cumulative frequency errors can lead to pulses appearing in incorrect time slots, resulting in erroneous demodulation of data by the pulse position modulation (PPM) scheme. A time-slot synchronization correction algorithm was proposed for PPM demodulation. This algorithm utilized a broad time-slot modulation technique to ensure that the information pulse is positioned at the center of the time slot. Real-time predictions of clock offset error are made by statistically analyzing the laser pulse position within a given time slot. Subsequently, the clock count is corrected using the obtained error information. The proposed algorithm effectively eliminates erroneous time slots resulting from the accumulation of frequency shift errors, thereby significantly reducing the bit error rate (BER) in the transmission of large-capacity data over long distances through atmosphere-sea laser communication.

Using the monthly data sets during 1951-2008 from standard upper-air stations, the spatial and seasonal distributions of upper turbulence over China are studied, and the atmospheric turbulence intensity at the pressure levels of 200hPa, 100hPa and 50hPa are given in this paper, providing scientific reference for relevant experiments. The intensity of atmospheric turbulence is closely related to height; at 200hPa pressure level, a downward trend of turbulence intensity from north to south is shown over China; at 100hPa, the turbulence is decreasing from south to north, weak turbulence occurs in the area north to 40°N; at 50hPa in near space, the turbulence in the west is slightly weaker than that in middle and east of the country. Influenced by seasonal variations of the mean circulations at each altitude, strong turbulence always occurs in winter while weak one in summer; but at 100hPa, the distribution of turbulence is evenly all over the country in summer, while weak turbulence occurs above the Tibetan Plateau and north of 40°N in winter; at 50hPa, the turbulence is weak in autumn, and a bit strong in summer. On the whole, the results could reflect the characteristic distributions of atmospheric optical turbulence in most general circumstances, and the most important value is to give the relative regional distributions of turbulence, to evaluate regional optical conditions on a macro scale.

Removing dense foreground occlusion from images and reconstructing the target of interest is a critical vision task. In previous studies, it was generally tackled through frame-based methods, but the performance was limited due to the lack of valid information. With the development of event cameras, their advantages in high temporal resolution and asynchronous response mechanism at each pixel have shown significant potential in various visual tasks. However, the event stream is plagued by multiple noise factors and static perceptual limitations, making it difficult to directly restore the local texture and absolute color of occluded objects. To overcome these challenges, we incorporate event stream information into the image frame restoration process to achieve a more effective occlusion removal. Specifically, we introduce a hybrid neural network for removing foreground occlusions from event-frame inputs, along with the design of an event stream encoder based on Spiking Neural Networks (SNN) and a Temporal Channel Attention Block (TCA) to enhance frame features. In addition, in order to significantly enhance the capability of occlusion removal, we introduce a General Restoration Block (GRB), which is applicable to both event data and frame data. Extensive experimental results indicate that the proposed method performs favorably against the state-of-the-art approaches.

In order to study the optical properties of aerosols in Qingdao, the temperature, humidity, wind speed and direction, and visibility were measured in Shinan District of Qingdao from 2019 to August 2020, and the seasonal variation characteristics of the optical thickness as well as Angstrom exponent in the area were analyzed using MODIS data. The analysis results found that particulate matter (PM) and relative humidity were the main factors affecting visibility. particulate matter concentration and visibility showed a negative exponential relationship. In the initial stage of PM governance, the improvement in visibility is not significant despite the reduction of particulate matter. However, once the PM concentration reaches a certain level, the improvement in visibility becomes remarkably evident. Analyzing the optical characteristics of Qingdao provides valuable insights into the local pollution control.

In the field of remote sensing image interpretation, utilizing Convolutional Neural Networks (CNNs) for building extraction is a highly significant task. End-to-end building extraction methods typically consist of two key components: the encoder and the decoder. However, during building extraction facilitated by the encoder, down-sampling operations often lead to a loss of boundary features in the segmented objects. Many of these lost features correspond to the boundaries of buildings, and the reduction of smaller-scale boundaries features diminishes the network's attention to building boundary, resulting in blurred architectural delineation. In this paper, we propose the Reshape Feature Distribution (RFD-Net) network to alleviate the problem of boundary blurriness. We embed a reshaping feature distribution module within the network, which manipulates the data distribution of feature values by compressing the maximum values and elevating the minimum values. This module can effectively increase the magnitude of feature values at positions corresponding to building boundaries in the feature maps, subsequently enhancing the network's attention to building boundaries and alleviating the problem of boundary blurriness. We conducted experiments on the WHU dataset, demonstrating the effectiveness of our proposed approach.

Multi-wavelength computational ghost imaging typically involves extensive data processing and computation, while also facing challenges such as low image reconstruction quality. Various methods have been reported for addressing these issues. In this paper, a method for multi-wavelength computational ghost imaging based on feature dimensionality reduction is proposed. This method enables the reconstruction of high-quality images while maintaining low-complexity computation and storage. The random measurement matrix is initially optimized through singular value decomposition, and the decomposed components are employed as illumination speckles. Following this, the reconstruction of the red, green, or blue component image of the target object is conducted using the second-order correlation function. Next, principal component analysis is applied to perform feature dimensionality reduction on the red, green, and blue component reconstruction images of the object. Simulation results demonstrate that our method can achieve high-quality computational ghost imaging while reducing computational complexity and storage requirements, creating favorable conditions for further optimization of computations.

Fast and accurate identification of unknown hazardous solid are of pivotal interest in public security and safety. In this research, the Raman spectra of ten dangerous were measured: four biotoxins (including aconitine, tetrodotoxin, α -conotoxin GI and ricin), six explosives (including Octogen, Hexanitrohexaazaisowurtzitane, Hexogen, Trinitrotoluene, Triacetone triperoxide and Black powder). The micro confocal Raman spectroscopy was used to obtain the spectrum data. Structural assignments to Raman bands observed in the spectrum were also proposed. On this basis, The principal component analysis (PCA) method is used to reduce the dimension of spectral data, and the linear discriminant analysis (LDA) pattern is developed based on Python language to establish recognition algorithm. The recognition algorithm based on the linear discriminant analysis could achieve a high recognition accuracy of 98.61%. Meanwhile, all the testing process could be completed within a few minutes without loss of samples. It suggested from this study that the combination of Raman spectroscopy of fingerprint characteristics and pattern recognition algorithm can be used for rapid screening of unknown compounds. Moreover, this method provides solutions for timely deletion of unknown compounds.

We verify a simple alternative method to estimate the Fried parameter over a horizontal propagation path using the refractive index measured by a pair of micro-thermometers. The results show a relatively reliable estimate, especially when the optical turbulence in the path is relatively strong. Moreover, we also discuss the relationship between the Fried parameter value with the overall intensity of optical turbulence and the length of the propagation path theoretically. The influence of these two factors shows a prominent exponential characteristic, which also can be speculated from the formula.

Obtaining high spatial resolution remote sensing images has always been one of the goals pursued in the field of remote sensing imaging. When the angular resolution of the remote sensor is constant, the closer the imaging distance, the higher the spatial resolution. For the remote sensor, the ground sample distance (GSD) at the nadir is closely related to the orbital height. Generally, the lower the orbital height, the easier it is to achieve high spatial resolution. With the rapid development of the ion thruster, spacecraft attitude adjustment technology and heat-resistant and corrosion-resistant materials, the use of ultra-low orbits to deploy micro-satellite, for earth observation and network collaborative information exchange has become an important direction in the development of aerospace technology. Based on the analysis of the development overview of typical foreign low-orbit satellites, combined with the analysis of orbit characteristics, this article summarizes the requirements of low-orbital satellites for remote sensor optical system. To meet the specific requirements, through comparative analysis of existing commonly used optical system forms, a new V-shaped high compression ratio optical system form is proposed, which greatly compresses the axial distance, and the focal length/length ratio is close to 10:1. A design example is given. Its technical specifications are as follows: focal length 4285mm, field of view angle 1.2°, F Number 8, the spectral range is 0.45 microns to 0.8 microns, the MTF value in the full field of view and the full spectrum is greater than 0.32 at the Nyquist frequency (83lp/mm), and the relative distortion is less than one ten thousandth. The simulation analysis results show that the imaging quality of the new V-shaped high compression ratio optical system proposed in this article is close to the diffraction limit, and the processing and assembly tolerance requirements are moderate. The engineering of the optical system is highly achievable, and it can well meet the application needs of low orbit.

The atmospheric boundary layer is the layer most closely associated with human life, and the occurrence and development of atmospheric optical turbulence in the atmospheric boundary layer are of great significance for atmospheric optical transport, etc. Meanwhile, the study of optical turbulence in the whole ocean environment is also of vital importance, and it is important to statistically analyze the variations of the atmospheric optical turbulence parameters by using the existing optical turbulence models due to the lack of ocean data. In this paper, the atmospheric turbulence parameters are estimated by different external scale models (HMNSP99, Dewan, HV and WSTG models), and the atmospheric refractive index structure constant（C²_n） computed by different models are compared by using the coastal sounding measured data, through error analysis and correlation studies, it has been found that the HV model changes with height in the atmospheric boundary layer, but cannot reflect the characteristics of the change of C²_n well. The HMNSP99 model is about one order of magnitude smaller than the measured data, while the WSTG model is about one order of magnitude larger than the measured data. However, the trends of the two models are in good agreement. In contrast, the Dewan model and the HMNSP99 model show good consistency with the measured data, and the correlation is above 0.6. The Dewan and HMNSP99 model are closer in magnitude to the measured data, therefore, when studying optical turbulence parameters in the atmospheric boundary layer, the Dewan and HMNSP99 models are more reliable. They can also provide key indicators for optical turbulence prediction and astronomical site selection.

In order to solve the problem that the surface deformation of large components cannot be photographed in real time due to the influence of assembly stress during assembly, a multi-source sensor surface deformation sensing and 3D imaging prediction technology for large components is proposed, which combined laser tracking, structured light detection and optical fiber sensing monitoring. Firstly, the multi-source sensor system is analyzed, and the multi-source sensor fusion sensing field is constructed. Secondly, the unified model of heterogeneous data is established to complete the accurate conversion of optical fiber monitoring wavelength to space point coordinates. Thirdly, multi-source data fusion is realized based on Gaussian process fusion algorithm to complete 3D imaging prediction of component surface deformation. Finally, a large skin component is taken as an example to simulate the assembly deformation experiment. The results show that the root-mean-square error is less than 0.1mm compared with the actual results. Based on the measured data of structured light scanning and multi-source sensing technology, a real-time sensing and prediction method for surface deformation imaging of large components is proposed. This method not only simplifies the scanning mode of structured light, but also provides a new idea for dynamic model building.

Scaling law and artificial intelligence model are two methods to quickly evaluate the far-field spot characteristics of laser propagation through turbulence. On the one hand, it is necessary to compare the evaluation accuracy of both models. On the other hand, the comparison between different models is only meaningful if each has their best accuracy. For specific scaling law model and artificial intelligence model, scaling exponents and hyperparameters determine the evaluation accuracy of the model to a certain extent. This paper discusses how to search better scaling exponents and hyperparameters to construct each model and compare the evaluation accuracy of both models. This paper first introduces the MRSS (Modified-Radius-Square-Sum) scaling law model and FT-Transformer (Feature Tokenizer + Transformer) model, and 3 hyperparameter (scaling exponent) optimization algorithms. Then, the accuracy of scaling exponents and hyperparameters obtained by different optimization algorithms is compared. Finally, the best scaling exponents and hyperparameters are used to construct each model. The results show that the TPE algorithm achieves better search results in fewer iterations for the FT-Transformer model, and the CmaEs algorithm achieves higher accuracy in more iterations for the scaling law model. The FT-Transformer model has better accuracy compared to the scaling law model, with the mean relative error of the far field effective radius and mean intensity is 1.32% and 2.66%, while those of scaling law model is 1.97% and 3.91% respectively.

The application of computational methods for enhancement the resolution of fluorescence microscopy images beyond the diffraction limit has emerged in recent years. Among them, super-resolution radial fluctuations (SRRF) and mean-shift super-resolution (MSSR) are two widely used representatives. However, these two methods are often unsatisfactory when dealing with low-quality fluorescence microscopy images, which are prone to artifacts as well as structure discontinuities. Here, we propose an effective computational method named morphological filtering and polynomial fitting (MFPF). MFPF equips excellent denoising and light-field balancing capabilities, which are beneficial for subsequent resolution enhancement. Our results validated on public BioSR dataset show that MFPF not only improves the quality of subcellular structure images such as microtubules and endoplasmic reticulum, but also attenuates the artifacts and discontinuities generated by SRRF radiality map analysis and single-frame MSSR reconstructions while maintaining the comparable resolution. Furthermore, it permits processing of low-quality fluorescence microscopy images, and would have vast applications in long-term super-resolution imaging of live cells with moderate image quality.

Hypersonic vehicles have a very complex trajectory and excellent maneuverability, which makes them effective in breaking the defence of enemy. Due to the strong and sustained infrared radiation emitted when they cruising rapidly in Near Space, researchers have paid attention to the infrared radiation characteristics of hypersonic targets and early warning detection mechanism. In order to acquire infrared radiation characteristics of hypersonic vehicles and collate research in infrared early warning detection technology phylogeny by the way, we have researched the Chinese and foreign progress of infrared radiation made by hypersonic target projectile, plume and the target entirety firstly; then the discussion of main method of infrared warning detection mechanism, technical models and conclusions as well. Lastly we discuss the main research directions in future by combining with the current research progress.

This article design and verify a visible light and infrared light fusion method strategy based on Curvelet-PCNN, which can give consideration to the total and local image feature. Then Curvelet-PCNN is compared with fusion method based on Curvelet-Cross, and get better effect under almost the same time consumption.

With the development of society, economy, and military, the demand for very short-term precipitation forecasting in far sea areas is becoming increasingly strong. In response to the low accuracy of precipitation forecasting, a correction method integrating FY-4 infrared cloud images for precipitation forecasting in the future 0~4 hour was proposed for the areas without weather radar such as the far sea. Firstly, based on the high-resolution prediction product from the numerical weather model WRF and the radiation transfer model RTTOV, the FY-4 infrared (10.3~11.3μm) forecast cloud image (bright temperature) was made by forward modeling method; Secondly, compared with the FY-4 observation cloud images, the evolution information of prediction errors of brightness temperature in the past -2~0 hours was obtained by using optical flow method. Next, three different schemes were proposed to apply the error evolution information of the predicted brightness temperature in the past -2~0 hours to correct the predicted brightness temperature in the future 0~4 hours; Finally, on the basis of brightness temperature correction with good results, three different correction schemes of forecast precipitation were proposed and analyzed. The results show that: 1) the average values of the root mean square error, average absolute error, and correlation coefficient of the predicted brightness temperature within 24 hours are 23.7K, 15.8K, and 0.45, respectively, showing a significant correlation between the predicted and observed brightness temperature; 2) after the correction, the average root mean square error of the predicted brightness temperature in the future 0~4 hours decreased by 5.4K, the average absolute error decreased by 3.4K, and the average correlation coefficient increased by 0.19; 3) after the correction, the TS score of predicted precipitation in the future 0~4 hours has significantly improved too. The research results will not only provide high-quality infrared forecast cloud images, but also improve the accuracy of very short-term precipitation forecasting in the far sea areas, and provide reference for improving the accuracy of precipitation forecasting in other regions such as the plateau and western regions of China.

Image enhancement plays an important role in the field of underwater vision. Numerous underwater image enhancement algorithms have been proposed in the last few years, which have achieved some good results in processing specific underwater images. However, the effectiveness of these algorithms to cope with different underwater environments remains uncertain. To address this issue, we propose a water body classification label based on scattering characteristics and construct a dataset with a large number of photos of experiments in different water conditions. Meanwhile, based on different types of water bodies we also trained a network model which has thirteen classifications. Using this dataset, we study comprehensively these underwater image enhancement algorithms qualitatively and quantitatively and match each type of underwater image with an optimal underwater image enhancement algorithm. An underwater image enhancement algorithm based on deep-learning water pre-classification is then proposed. This adapted algorithm is applied to process real underwater images captured by the underwater robot and obtains good processing results. It also contributes to further research on underwater image enhancement.

Remotely Operated Vehicles (ROV) is playing an increasingly important role in the process of human exploration of the ocean, and its motion control system needs to be stable, reliable, rapidly responsible and easy to use. This paper designs and implements a new type of motion control system for ROV. The architecture of control system includes QT upper controller to input motion control instructions and display the status information, companion machine Raspberry PI used as a communicate transfer station, and runs the designed control method, bottom controller MCU for controlling the speed of motors and receiving sensor data and sending them upward. Meanwhile the kinematics and dynamics model of ROV are established. Then, the model-based PID controller of depth and three attitude angles in YPR-model is designed and implemented. Its performance is verified by simulation in MATLAB. Finally, in swimming pool the experiment of depth and three attitude angles control using model-based PID and model-free PID are conducted and compared. Through experimental analysis, the practicability, stability and reliability of the motion control system and the model-based PID control method are verified.

Estuary is an important part of the coastal zone. It is the junction of rivers and ocean. It is not only the end of the material in rivers, but also the beginning of the material in ocean, and an important place for the exchange of material and energy between continents and oceans. The Yongding New River flows into the Bohai Bay, which is an important area for the construction of the Bohai Rim Economic Zone. The migration law of chlorophyll concentration and its influencing factors are the core issues of estuarine research. Remote sensing inversion can realize dynamic, continuous and synchronous observation of large areas of water, and quickly obtain the spatiotemporal distribution of chlorophyll concentration. In this study, the Sentinel-2 MSI data from 2017 to 2021 were used to retrieve the chlorophyll concentration in the study area. Based on these data, the spatiotemporal variation of chlorophyll concentration in different seasons and its influencing factors were analyzed. The results show that the chlorophyll concentration in the study area has obvious temporal and spatial distribution rules, which is higher in spring and winter, lower in Summer and Autumn. The three elements of Sea Surface Temperature (SST), Photosynthetically Active Radiation (PAR) and Wind Speed (WS) all have an impact on the spatiotemporal distribution of chlorophyll concentration.

Due to variations of influencing factors and atmospheric effects, the propagation efficiency (transmittance, thermal distortion parameter and 63.2% encircled average power density) of high-energy laser propagating in the atmosphere is uncertain. In this paper, aiming to evaluate the uncertainty of propagation efficiency and identify the main influencing factors, the following research is made. (1) The scaling law is established through numerical simulation, which is suitable for the Gaussian waveform laser and considers the interaction between different effects. (2) The probability distribution characteristics and uncertainty of propagation efficiency are evaluated in the horizontal propagation scenario by the Latin hypercube sampling method. (3) The Elementary Effect Test is applied, with the aim to give the parameters prioritization and identify the crucial parameters affecting encircled average power density. The results show that the uncertainty and parameters prioritization of propagation efficiency vary with the propagation distance. Considering the results of the Elementary Effect Test at different distances, the crucial parameters for 63.2% encircled average power density are transverse wind speed, absorption coefficient, power, and initial beam quality. This research is of great significance for the application of laser systems.

In the application scenarios of high dynamic natural disasters such as earthquakes, volcanoes, tsunamis and hurricanes, the spectral information and time sensitivity of detection images are required to be high. This paper discusses the background of multi-source information detection of optical SAR and the progress of integrated technology, compares and analyzes the characteristics of the existing integrated technology system, and puts forward a new optics and SAR coaperture Imaging System. The SAR payload part uses light and small planar phased array antenna, which has excellent performance, but as part of an integrated system, and there is no matching optical system. Therefore, a new optical imaging scheme is proposed. Using a flat-plate grating primary mirror and a SAR detection mechanism in parallel layer in the aperture direction, so as to embed a planar phased array antenna in the optical system. The system can realize the co-aperture coupling matching between optical load and radar load detection area without affecting the reception of each signal. The planar grating primary mirror realize optical folding while realizing large equivalent aperture, and provide spectral resolution. The system is small in size and light in weight, which can save more platform space and provide theoretical basis and technical support for the application of optical-SAR image fusion in orbit.

Polarimetric inverse synthetic aperture radar (ISAR), with its ability to operate in all conditions, plays an important role in space surveillance. The compact polarimetric mode balances hardware complexity and polarimetric information, commonly equipped with ISAR systems. However, the generation of high-resolution ISAR images usually requires a large bandwidth and coherent integration angle, which is constrained by the equipment’s physical conditions. At present, supervised learning methods are often used for image super-resolution in computer vision. However, super-resolution performance is often hampered by the occurrence of artifacts and the inadequate consideration of low-frequency information in low-resolution image data. To address these limitations, this work presents a semantic information guided semi-supervised deep-learning method. This framework incorporates implicit neural representation to extract and better utilize information from low-resolution ISAR images. In addition, semantic and super-resolution information are integrated to regulate the training process. Datasets comprising images and semantic information of compact polarimetric ISAR for satellite targets are constructed. The proposed method yields more elaborate super-resolution results with fewer artifacts. Quantitative evaluations are also carried out using the Peak-Signal-to-Noise (PSNR) metric. Compared with the typical methods, the proposed approach achieves superior super-resolution performance, with a performance improvement of at least 1.394 dB.

In recent years, there has been a significant focus on using agile modes in high-resolution optical satellites. These modes, such as active push-broom, multi-angle stereo imaging, and non-along-track imaging, take advantage of the satellite's maneuverability to improve the balance between spatial resolution and time resolution, thus enhancing imaging efficiency. However, compared to traditional passive push-broom imaging, these methods can result in a decrease in image quality. For instance, a drift angle can cause a shift in the image's nadir direction, leading to a reduction in the modulation transfer function (MTF). While satellites have measures to correct for drift angles, the impact of drift angle on wide-field optical remote sensing satellite imaging cannot be ignored, especially during active push-broom imaging processes that involve large attitude maneuvers and low precision in satellite attitude control. The residual effect of correcting for drift angle varies across different points in the full field of view under different non-along-track imaging conditions. Generally speaking, the magnitude of residuals is directly proportional to the angle of the track and the proximity to the edge of the field of view. A quantitative analysis model has been developed to evaluate the imaging quality degradation under various non-along-track conditions for different parameter designs. This model takes into account the distribution of residuals after correcting for the drift angle, which causes an uneven decrease in MTF across the field of view. It enables the selection of optimal parameter combinations for multiple imaging parameters and task planning, ensuring that the imaging quality in the target area meets user requirements. Using the orbital elements and a set of angles and starting point latitude and longitude between 0° and 330° spaced 30° apart for non-along-track imaging tasks, 13 unequally spaced sampling points in the full field of view were selected to simulate the residual drift angle under typical non-along-track imaging conditions. The residual drift angle distribution map was drawn in each image and further, the MTF (Modulation Transfer Function) map was calculated. These maps represent the distribution of image quality within the images and validate the effectiveness of the quantitative analysis model. The analysis results are valuable for ensuring the imaging quality of high-resolution optical satellites during agile imaging and can be further extended to develop image-quality-oriented agile mission planning methods and other applications.

The rise in greenhouse gas concentrations has been identified as a primary driver of global warming, leading to adverse effects such as rising sea levels and droughts. In response, understanding the dynamics of greenhouse gas concentration changes has become pivotal in the quest to effectively combat climate change and mitigate the adverse effects of global warming. This study utilizes data from the GOSAT satellite to analyze the trends in global CO₂ and CH₄ concentrations from 2010 to 2022. Furthermore, the time series and seasonal variation characteristics of greenhouse gas concentrations of CO₂ and CH₄ in the Hefei area were studied, combined with the geographical environment of Hefei. To enhance comprehension, the article also assembles an HYSPLIT backward trajectory model to scrutinize the latent influences exerted by monsoon transport and atmospheric boundary layer conditions on greenhouse gas distributions. Over the course of the decade from 2010 to 2022, greenhouse gas concentrations in the Hefei region exhibited an unwavering upward trajectory, punctuated by conspicuous seasonal fluctuations, showcasing distinct seasonal variations that aligned with the observations of the ground-based observation network TCCON. The concentrations of CO₂ surged from 391.05 ppm to 417.98ppm, signifying a net gain of 26.930ppm, corresponding to an annual growth rate of approximately 2.4 ppm. Similarly, CH₄ concentrations underwent a net increase of 72 ppb, characterizing an annual growth rate of about 10.4 ppb. These figures underscore the relentless ascent of greenhouse gas concentrations, warranting immediate attention and action. The concentrations of greenhouse gases are subjected to a plethora of factors, encompassing both local biogenic and non-biogenic sources, the intricate patterns of monsoon-driven atmospheric transport, and the unique characteristics of the atmospheric boundary layer. The findings emanating from this comprehensive study are poised to serve as the bedrock upon which Hefei City can formulate and refine its strategies for greenhouse gas emission reduction. Furthermore, the study emphasizes the impact of monsoon transport patterns and atmospheric boundary layer conditions, which can significantly affect the dispersion and accumulation of greenhouse gases in the region. Understanding these factors is crucial for devising effective strategies to mitigate greenhouse gas emissions.

As aerospace detection technology evolves, limb optical detection is increasingly becoming a focal point of research, attributed to its high spatial coverage and elevated vertical resolution. Based on the SCIATRAN limb atmospheric radiation model, simulation analyses of limb radiation transmission characteristics in the middle and upper atmosphere were conducted for both clear and cloudy conditions in the visible to near-infrared spectrum. The study results indicate that observational tangent height and solar zenith angle are important parameters affecting limb radiation brightness in the middle and upper atmosphere, with limb radiation brightness showing a decreasing trend as tangent height increases; in the visible light spectrum, it gradually weakens with increasing solar zenith angle, but in the near-infrared spectrum, it first decreases and then increases. The presence of aerosols and cirrus clouds significantly affects the mid-to-high altitude atmospheric limb radiation brightness. Under stratospheric aerosol conditions, radiation brightness can increase up to 2744.31% compared to background conditions, and under cirrus clouds with an optical thickness of 1, the increase in radiation brightness can be up to 13.78 times compared to clear sky conditions. The study delves into and analyzes the impact of particle optical properties on limb atmospheric background radiation, offering theoretical and data foundations for comprehending its spectral characteristics and designing limb detectors.

In order to improve the performance of multi-frame blind deconvolution algorithm, the analysis was conducted on the image restoration quality and convergence rate of the multi-frame blind deconvolution algorithm using Conjugate Gradient + Brent, Conjugate Gradient + Dbrent, Conjugate Gradient + Macopt, and L-BFGS + Wolfe combination optimization algorithms. The mathematical principles of above optimization algorithms were elaborated in detail, and they were introduced into the multi-frame blind deconvolution algorithm to achieve high quality restored images. Theoretical and experimental results indicate that the L-BFGS + Wolfe combination algorithm has the fastest convergence rate, but the restoration quality is lower compared to the other combination algorithms; Compared with the other combination algorithms, the Conjugate Gradient + Brent/Dbrent combination algorithm can obtain higher quality restored images, but its convergence rate is slower; The convergence rate and restoring quality of the Conjugate Gradient +Macopt combination algorithm are between L-BFGS + Wolfe and Conjugate Gradient + Brent/Dbrent.

With the maturity of LiDAR technology, LiDAR point cloud segmentation has been widely applied in automatic inspection of power lines. However, in weather scenarios such as rain, snow, and dust, lidar data noise and dynamically changing power line data can be generated, resulting in a decrease in power line extraction efficiency. The commonly used airborne LiDAR can only work in sunny weather conditions, and in order to improve the accuracy of point cloud power line extraction, the data collected on board needs to be preprocessed to obtain complete scene point cloud data, which cannot meet the requirement of automatic inspection of power lines.In order to solve the problems of real-time monitoring of airborne LiDAR data and low accuracy in extracting power line point clouds under different weather scenarios, this paper proposes a point cloud power line extraction method based on the improved DBSCAN algorithm, starting from the data features of fixed LiDAR real-time scanning point clouds. Firstly, the cloth filtering method is used to filter out ground points and obtain non ground point clouds; On this basis, based on the spatial relative density characteristics of non-ground point clouds, target data such as traverse points and tower points are roughly extracted from non-ground points; Then, the distribution characteristics of the elevation point cloud are used to identify the tower, and the maximum width of the tower is used to segment the power lines within the range of the tower. Then, based on the data characteristics of the point cloud, the density clustering parameters are continuously modified to further improve the accuracy of power line point cloud segmentation. In order to verify the effectiveness of the algorithm, point cloud power line point cloud segmentation experiments were conducted in different meteorological environments, and compared with European clustering segmentation and regional growth algorithms. The experimental results show that the improved DBSCAN algorithm proposed in this paper has the best segmentation performance for power line point clouds in complex weather scenarios, which is basically consistent with sunny conditions and can meet the actual power inspection needs.

In wide-angle camera target detection and tracking systems, distortion correction of infrared images is required. The interpolation algorithm is an important part of distortion correction, and the current interpolation algorithm has certain shortcomings in calculation efficiency and interpolation effect. To solve this situation, an interpolation method based on edge preservation is proposed. This method compares the local standard deviation of the interpolation region with the threshold to determine whether the current interpolation region belongs to a smooth region or an edge region, and uses the neighborhood pixel weighted sum instead and the pixel value in the maximum gradient direction instead. Experiments show that this method can better maintain the high-frequency information in the image, and reduce the calculation amount compared with bicubic interpolation.

Lidar is an effective approach for detecting the Planetary Boundary Layer Height (PBLH). Traditional lidar algorithms are prone to interference and misjudgment under complex atmospheric conditions such as cloud layers and suspended aerosol layers. Some studies have proposed the combined use of lidar and thermodynamic remote sensing to retrieve PBLH, which has improved the accuracy of retrieval. However, fundamentally, traditional algorithms are still utilized, and the retrieval results are still susceptible to the influence of complex conditions. This paper proposes a machine learning based PBLH retrieval model that integrates lidar and thermodynamic remote sensing data as the training dataset to predict PBLH. Experimental results demonstrate that, compared to traditional and combined algorithms, the proposed method estimates PBLH close to the height measured by radiosonde with minimal error. It is evident that the proposed method can reliably retrieve PBLH with minimal susceptibility to external interference.

Separation of leaves and woody materials is crucial for estimation of biophysical attributes of trees such as leaf area index and above-ground biomass. Segmentation based on images from traditional cameras is difficult in low-light level conditions like high canopy density areas and understory vegetation in rain forests. Point clouds from terrestrial laser scanning (TLS) LIDAR are also used for canopy quantitative analysis, but it suffers from low spatial-resolution for leafwood separation. To solve the problems mentioned above, we present a method of wood-leaf separation method based on a dual-wavelength active range-gated imaging system to separate leaves, woody elements, and background. In our method, two overlapped near infrared gated images at the wavelength of 808nm are obtained with background filtered by gated viewing, and a green-channel image is grasped at the illumination of 530nm LED. Then through data preprocessing, these images are input into our separation algorithm. Our separation method uses a peak-search formula to find the target peak in the histogram of an image, and the threshold is the local minimum at the right of the target peak. After segmentation by thresholding, a woody area mask is obtained. Combined with the point clouds reconstructed from gated images, separation on the point clouds is available. We have collected images of vegetation and performed manual separation to test our method. The results show that our method is capable to make accurate classification of leaves, woody elements and background.

This study explores the maximum bathymetric capabilities of the ICESat-2 in coastal environments, with a particular focus on the impact of cirrus cloud thickness. Utilizing MODTRAN simulations for atmospheric transmittance under various cloud conditions, we integrate these with a maximum bathymetric depth model to quantitatively assess this impact. Our results indicate a significant decrease in detection depth with increasing cloud thickness, culminating in complete signal attenuation at cloud thicknesses of 3-4 km. Furthermore, simulations reveal that the FF phase function model outperforms OTHG and TTHG models, exhibiting the lowest Mean Absolute Error (MAE) and Mean Relative Error (MRE) in the Puerto Rico and Virgin Islands study sub-areas. This research provides critical insights into the capabilities and limitations of spaceborne lidar in coastal bathymetry, highlighting the importance of atmospheric conditions in remote sensing applications.

When the airborne infrared imaging system searches and strikes ground targets, it is mainly used to extract target information from complex background interference images on the ground and transmit it to the driving or automatic guidance system, forming the position and motion information of the missile relative to the target, in order to better track the target and output information to control the missile's flight. The airborne infrared imaging system detects, recognizes, and the tracking performance is closely related to the infrared radiation characteristics of the background. This article focuses on the research of multiband characteristics of military application targets and backgrounds. On the basis of unified standards, a database management system is established to meet the management and query functions of measured infrared characteristics data of typical backgrounds such as deserts, grasslands, mountains, and forests in different seasons and at different times, as well as the development of an infrared imaging detection performance evaluation system, We studied the detection and recognition probabilities of infrared imaging systems for targets at different distances in different background environments, and provided theoretical calculation results for the detection range of a certain type of installed airborne infrared imaging detection system for 2m * 2m sized targets in the 3.7μm-4.8μm band at 9:00, 16:00, and 21:00 in a summer grassland background in a certain area. This provides support for the design and development of infrared imaging weapons and equipment.

Manganese nodules, widely distributed across the deep-sea floor, are emerging as a significant potential mineral resource. Addressing the need for improved exploration and classification methods, this study explores the application of underwater hyperspectral imaging technology for the detection and classification of manganese nodule ore and other rock types. The system, operating within a spectral range of 400 - 1000 nm and achieving a spectral resolution of less than 5 nm, captures the spectral characteristics and spatial information of manganese nodule ores. Different classifiers, including Spectral Angle Mapper (SAM), Support Vector Machine (SVM), and Convolutional Neural Networks (2D-CNN and 3DCNN) to analyze the spectral data. Our results indicate that the four classifiers can effectively achieve ore classification, and CNN-based classifiers significantly outperform traditional SAM and SVM methods. The 2D-CNN model achieved the highest OA at 92.27%, closely followed by the 3D-CNN model at 91.77%. Our findings demonstrate the potential of underwater hyperspectral imaging combined with advanced machine learning techniques in marine mineral detection and environmental monitoring.