In low-light conditions, high resolution remote sensing cameras usually require a long exposure time, during this process, the relative motion between satellites and ground objects leads to a degradation in imaging quality. Addressing this issue, this paper proposes a low-light staring remote sensing imaging technology based on compressive imaging theory. This technique slices the long exposure process into many short temporal frames, using spatial-variant mask to code each frame. these modulated time frames are collected into compressive measurements that contain spatio-temporal information. Then, a specific algorithm is used to reconstruct short time frames by compressed measurements and masks, finally solve the motion blur problem caused by long exposure, Utilizing this technology, this paper simulated experiments and analyses of factors such as mask form, target motion speed, satellite platform vibration, and encoding time error. The main experimental conclusions are as follows:1. Random binary mask exhibits better reconstruction performance compared to random uniform mask when the number of frames is less than 10; 2.For moving targets, under the pupil radiance is 1×10-2W/(m-2×sr), clear imaging of cars traveling at 80 km/h can be achieved (PSNR exceeding 35 dB); 3.When the maximum platform vibration amplitude is approximately 1/4 of a pixel, The PSNR can reach 40dB, meeting the requirements of high-resolution remote sensing imaging; 4. Introducing encoding time error causes PSNR degradation of about 1 dB, with minimal impact on imaging quality. These research results contribute a certain theoretical basis and engineering reference for the study of weak-light remote sensing imaging.
Large aperture space telescope is a significant scientific instrument to achieve high resolution remote sensing and high sensitivity detection. The current approach to space telescope is designed to meet the volume and mass requirements associated with a single launch vehicle, one potential approach that addresses these current challenges and achieves that future is In-Space Assembly. First of all, the development status and technologies of In-Space Assembly Telescope (ISAT) are reviewed, and some typical projects are presented to show the future application. Then, the key technologies involved are analyzed from multiple aspects: large truss structure, robot technologies and lightweight segmented mirrors. On this basis, several potential technical difficulties and challenges in the future are given, which need to be further developed to promote the capability of space technology. Finally, the future development trends and technologies of ISAT are prospected, which may provide a reference for evolution of large aperture space telescope.
The existence of diffraction limits forces optical remote sensing to develop towards a super-large aperture, but it is limited by the production process, manufacturing cost and carrying capacity. Although block expandable optical imaging technology, thin film diffraction imaging, optical synthetic aperture imaging and other technologies have been developed, these technologies have high requirements on process level and control accuracy, and are difficult to implement. Based on the Arago spot, this paper proposes a new ultra-high-resolution imaging technology suitable for space-based optical remote sensing, replacing the traditional optical system with a visor disk to achieve the lowest-cost high-resolution observation. Simulation results show that the visor disk with a diameter of 100 meters is deployed at the Lagrangian point 450,000 kilometers away from the Earth, with a resolution of 2.5 meters, which can achieve high-resolution observations of Earth-Moon space.
Aimed at key problems the system of 1:5000 scale space stereo mapping and the shortage of the surveying capability of urban area, in regard of the performance index and the surveying systems of the existing domestic optical mapping satellites are unable to meet the demand of the large scale stereo mapping, it is urgent to develop the very high accuracy space photogrammetric satellite system which has a 1:5000 scale (or larger).The new surveying systems of double baseline stereo photogrammetric mode with combined of linear array sensor and area array sensor was proposed, which aims at solving the problems of barriers, distortions and radiation differences in complex ground object mapping for the existing space stereo mapping technology. Based on collinearity equation, double baseline stereo photogrammetric method and the model of combined adjustment were presented, systematic error compensation for this model was analyzed, position precision of double baseline stereo photogrammetry based on both simulated images and images acquired under lab conditions was studied. The laboratory tests showed that camera geometric calibration accuracy is better than 1μm, the height positioning accuracy is better than 1.5GSD with GCPs. The results showed that the mode of combined of one linear array sensor and one plane array sensor had higher positioning precision. Explore the new system of 1:5000 scale very high accuracy space stereo mapping can provide available new technologies and strategies for achieving demotic very high accuracy space stereo mapping.
For high spatial resolution optical remote sensing imaging system, the performances of sampling imaging system are traditionally designed and evaluated according to the system SNR and the system MTF at Nyquist frequency. On the basis of information theory, this paper proposed an optimization design and evaluation specification based on full remote sensing imaging chain: information density. It combined various imaging quality parameters, such as MTF, SNR and sideband aliasing, as well as included the influences of the scene, atmosphere, remote sensor and satellite platform in in-orbit imaging chain to the imaging quality. The system designs and experiments under different resolutions were also conducted. The experiment result showed that information density can be used to evaluate the performance of sampling imaging system and direct the optimization design of optical remote sensing system with a high spatial resolution.
Remote sensing features are varied and complicated. There is no comprehensive coverage dictionary for reconstruction. The reconstruction precision is not guaranteed. Aiming at the above problems, a novel reconstruction method with multiple compressed sensing data based on energy compensation is proposed in this paper. The multiple measured data and multiple coding matrices compose the reconstruction equation. It is locally solved through the Orthogonal Matching Pursuit (OMP) algorithm. Then the initial reconstruction image is obtained. Further assuming the local image patches have the same compensation gray value, the mathematical model of compensation value is constructed by minimizing the error of multiple estimated measured values and actual measured values. After solving the minimization, the compensation values are added to the initial reconstruction image. Then the final energy compensation image is obtained. The experiments prove that the energy compensation method is superior to those without compensation. Our method is more suitable for remote sensing features.
In this paper, a new image de-noising algorithm based on series connected pulse-coupled neural networks (PCNN)
model is presented. Traditional PCNN is a single layer model, which is suitable for real-time image processing. In this
article, a new improved PCNN model called the 'series connected PCNN' is proposed, and the traditional PCNN model
has been rationally simplified. The simplified 'series connected PCNN' model has less iterative times, and it's more
sensitive to image edges than the traditional model. The experimental results show that the new algorithm is very
effective and provides better performance in protecting image edges compared with the median filter.
In this paper, a novel edge-parameter analysis method of the blu' identification based on the single-threshold Pulse
Coupled Neural Networks (PCNN) model is proposed for image de-blurring application. It suits to the identification of
the horizontal linear motion blur. This new identification method not only improves on the traditional PCNN, but also
uses the normalized local entropy. On the one hand, the new method uses the local entropy which is normalized between
0 and 255. On the other hand, a new model called the single-threshold PCNN is proposed in this article. Comparing with
the traditional PCNN, the improved one calculates faster, and it is more sensitive to the image edges. The experimental
results which are obtained from the different images and the same image with the different resolution show that the new
algorithm is very effective and the curve is the very steady graph. The identification precision is about 4 to 30 pixels.
In this paper, a novel contourlet transform based on median filter is proposed. By using a novel median pyramidal
decomposition, the noise distributing is analyzed for the image distorted by salt-and-pepper noise and Gaussian noise
respectively. Comparing the Probability-Density -Functions of the detail coefficients of the each corresponding layer, it
is found that these two kinds of noise mainly concentrate on the bottom high frequency layer. So a majority of noises can
be removed by denoting zero the bottom layer coefficient. Median-Contourlet transform is completed when the second
layer and other high frequency image is calculated by PDFB(Pyramidal Directional Filter Bank). After analysing of
Contourlet coefficients, we select the best threshold to remove further the noises. Applying the same denoising method
to images, the Median-Contourlet achieves obvious improvement in both subjective visual effect and SNR comparing
with traditional contourlet transform.
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