An Infrared Imaging Pretreatment System was designed based on Xilinx Zynq-7000 Extensible Processing Platform (EPP). Zynq-7000 integrate a dual-core ARM Cortex-A9 based processing system (PS) and programmable logic (PL) in a single device. In this design, PL was developed for IRFPA video signal acquisition, and used VIDEO_IN, VIDEO_OUT, AXI-VDMA IP core to build high speed data channel between PS and PL. PS was developed for Imaging Pretreatment such as Non-uniformity correction, Blind pixels compensation and Image enhancement, using DDR3 for PS’ external memory and PL for Camera Link/PAL video output. It’s optimized for powerful, high real-time, small size, low-power and high reliability, to reach the goal of mass production in work.
In order to enhance the robustness of IR fast small target tracking, a novel mean shift tracking algorithm using improved similarity measure of is proposed. Firstly, problems of local background interfering in original mean shift algorithm for tracking fast motion small target is analyzed, and the reasons is located in the Bhattacharyya coefficient similarity measure expression for all gray weights of components are same, which cannot reflect the advantage contribution of the small target’s gray component in the process of measuring similarity, causing serious interference of the background in the tracking process, leaving the algorithm converging easily. Therefore, to solve this problem, the improvements Bhattacharyya coefficient similarity measure with the local background information fused is proposed. Then, shift vector is deduced in the framework of mean shift by regarding Bhattacharyya coefficients as the similarity measure.In shifting process, the robustness of the small target tracking is improved effectively according to target gray level of large membership degree with high shift weight, and vice versa with low shift weight, which the background interference is suppressed to some extent. In sake of verifying the performance of the proposed algorithm, the classical mean shift algorithm and the algorithm of this paper is used in the target tracking simulation experiment, as well as the infrared image sequences containing the small fast targets of uncooled infrared camera is used. Finally the experimental result indicates that the performance of tracking the small fast target in IR images is robust.
In order to meet the requirements of identification of satellite local targets, a new method based on combined feature
metrics is proposed. Firstly, the geometric features of satellite local targets including body, solar panel and antenna are
analyzed respectively, and then the cluster of each component are constructed based on the combined feature metrics of
mathematical morphology. Then the corresponding fractal clustering criterions are given. A cluster model is established,
which determines the component classification according to weighted combination of the fractal geometric features. On
this basis, the identified targets in the satellite image can be recognized by computing the matching probabilities between
the identified targets and the clustered ones, which are weighted combinations of the component fractal feature metrics
defined in the model. Moreover, the weights are iteratively selected through particle swarm optimization to promote
recognition accuracy. Finally, the performance of the identification algorithm is analyzed and verified. Experimental
results indicate that the algorithm is able to identify the satellite body, solar panel and antenna accurately with
identification probability up to 95%, and has high computing efficiency. The proposed method can be applied to identify
on-orbit satellite local targets and possesses potential application prospects on spatial target detection and identification.
A new method is proposed to solve the problem of image restoration of high resolution TDICCD camera due to satellite
vibrations, which considers image blur and irregular sampling geometric quality degradation simultaneously. Firstly, the
image quality degradation process is analyzed according to imaging characteristics of TDICCD camera, owing to image
motions during TDICCD integration time induced by satellite vibrations. In addition, the vibration simulation model is
established, and the irregular sampling degradation process of geometric quality is mathematically modeled using
bicubic Hermite interpolation. Subsequently, a full image degradation model is developed combined with blurred and
noisy degradation process. On this basis, a new method of image restoration is presented, which can implement not only
deblurring but also irregular to regular sampling. Finally, the method is verified using real remote sensing images, and
compared with the recent restoration methods. Experimental results indicate that the proposed method performs well,
and the Structural Similarity between the restored and ideal images are greater than 0.9 in the case of seriously blurred,
irregularly sampled and noisy images. The proposed method can be applied to restore high resolution on-orbit satellite
images effectively.
The modeling and the validation methods of the spectral BRDF on the material surface of space target were presented. First, the microscopic characteristics of the space targets’ material surface were analyzed based on fiber-optic spectrometer using to measure the direction reflectivity of the typical materials surface. To determine the material surface of space target is isotropic, atomic force microscopy was used to measure the material surface structure of space target and obtain Gaussian distribution model of microscopic surface element height. Then, the spectral BRDF model based on that the characteristics of the material surface were isotropic and the surface micro-facet with the Gaussian distribution which we obtained was constructed. The model characterizes smooth and rough surface well for describing the material surface of the space target appropriately. Finally, a spectral BRDF measurement platform in a laboratory was set up, which contains tungsten halogen lamp lighting system, fiber optic spectrometer detection system and measuring mechanical systems with controlling the entire experimental measurement and collecting measurement data by computers automatically. Yellow thermal control material and solar cell were measured with the spectral BRDF, which showed the relationship between the reflection angle and BRDF values at three wavelengths in 380nm, 550nm, 780nm, and the difference between theoretical model values and the measured data was evaluated by relative RMS error. Data analysis shows that the relative RMS error is less than 6%, which verified the correctness of the spectral BRDF model.
In order to improve the performance of heterogeneous image matching and registration, the Weighted Voting Accumulation Measure(WVAM) based on the edge feature and image registration algorithm based on the steepest descent of the likelihood function are proposed. The WVAM is capable of resisting the interference of noise and the similarity region and can achieve matching location of template. On this basis, the likelihood function of edge sets registration is established on the basis of Gauss Mixture Model (GMM) of point sets. In order to achieve the registration between the template and matching area, and resolve the optimum transformation parameter by using the steepest descent method, the likelihood function is regarded as objective function and the affine transformation parameter is regarded as the optimization variance. The results of simulation experiments of this algorithm proved that the good performance of template and registration.
Scattering phase function on horizontally oriented ice particles near the specular reflective direction is analytically modeled using a mixed method combining direct reflection and Fraunhofer diffraction components, where particles are simply treated as circular facets and the effect of fluttering is introduced under the assumption of Gauss distribution. The obtained model expression reveals that the essence of far-field scattering around specular direction is the diffraction pattern modulated by fluttered geometric reflection. Four groups of experiments are designed to validate this model at different wavelengths and incidence angles, and the calculated phase functions present good agreement both in distributions and peak values with that of T-matrix method in conjunction with a Monte Carlo stochastic process.
Under the application background of sea-surface target surveillance based on optical remote sensing image, automatic sea-surface ship target recognition with complicated background is discussed in this paper. The technology this article focused on is divided into two parts, feature classification training and component class discrimination. In the feature classification training process, large numbers of sample images are used for feature selection and classifier determination of ship targets and false targets. Component tree characteristics discrimination achieves extraction of suspected target areas from complicated remote sensing image, and their features are entered to Fisher for ship target recognition. Experimental results show that the method discussed in this paper can deal with complex sea surface environment, and can avoid the interference of cloud cover, sea clutter and islands. The method can effectively achieve ship target recognition in complex sea background.
The wavefront function can be achieved by fitting the optical surfaces date using Zernike polynomials because of the corresponding relation between Zernike polynomials and Seidel aberrations. In this paper, the reason of the stable solution cannot be achieved when proceed to fit wavefront by least square, Gram-Schmidt orthogonalization and Householder transformation is deduced in theory. The Zernike coefficients fitting method based total variation (TV) regularization is presented to resolve the instability of numerical solution because of there are errors in phase values obtained by optimization algorithm in Least Square, Gram-Schmidt orthogonalization and Householder transformation. The solving model of Zernike coefficients is developed, and the regularization term is introduced in solving model, then the L-curve method is applied to determine the regularization parameter and the modified steepest descent method is applied to solve Zernike coefficients. The simulation experiment shows that the proposed algorithm can be achieve the stable fitting coefficients with the error on fitting data.
A spatial-temporal detection target method is proposed to detect weak point target with slow velocity in infrared sequences evolving cloud clutter. Frist of all, a temporal filter for detecting point target called triple temporal filter (TTF) is introduced. Since theoretical analysis shows that TTF has a poor performance under temporal noise, a nonlinear spatial-temporal filter by neighbor pixels in prior and posterior frames ,which takes every possible target trace account to suppress noise before coming into recursion. Then TTF output by positive and inverse sequence order form nonlinear spatial-temporal filter fuse with liner principle for detecting weak target is put forward, which called bilateral TTF in this paper. Finally its performance is analysis. The results of experiment shows that compared to original TTF, the proposed method achieves a higher signal-to-clutter ratio gain, which is effectively detecting dim target when target signal-to clutter down to 3 or lower with a low moving velocity.
KEYWORDS: Modulation transfer functions, Signal to noise ratio, Mathematical modeling, Remote sensing, Image processing, Digital image processing, Electro optical modeling, Digital photography, Distortion, Detection and tracking algorithms
The determination of conjugate points in a stereo image pair, i.e. image matching, is the critical step to realize automatic
surveying and recognition in digital photogrammetric processing. The accuracy of image matching is closely related to
specific matching algorithm as well as images. In this paper, the qualitative and quantitative relationships between the
matching accuracy and the image metrics are studied at the basic of Least Squares Image Matching algorithm (LSIMA).
Firstly, the algorithm is deduced mathematically, and then the main image metrics affecting the matching accuracy are
presented, including total variation (TV) metric and difference of signal-to-noise ratio (DSNR) metric. Subsequently,
variations of matching accuracy with TV and DSNR are analyzed, and mathematical model between them is developed.
Studies show that the matching accuracy presents the natural exponential rule along with TV and DSNR of image pairs.
Besides, parameters of the model are estimated and the model is verified by simulation experiments. Finally, the
correctness of the model is verified using real remote sensing images. Experimental results demonstrate the robustness
and accuracy of the proposed model.
In order to detect small target in non-stationary complex background, this paper summarizes the previous representative
results about the image complex degree metrics, and focuses on analyzing the disadvantage of weighted information
entropy (WIE) in the application of adaptively detecting small target in frequency domain. We then introduce clustering
and classification of feature vectors of pixels to the detection of small targets, and clustering degree of feature vectors of
background pixels and the dispersion degree of feature vectors of target pixels outside the background clustering in
feature space are described by constructed Statistical Distance Weighted Information Entropy (SDWIE). Then the
adaptive small target detection algorithm based on SDWIE is proposed in this paper. The validity of this algorithm was
demonstrated by actual experiments.
A novel automatic target recognition algorithm based on statistical dispersion of infrared multispectral images(SDOIMI)
is proposed. Firstly, infrared multispectral characteristic matrix of the scenario is constructed based on infrared
multispectral characteristic information (such as radiation intensity and spectral distribution etc.) of targets, background
and decoys. Then the infrared multispectral characteristic matrix of targets is reconstructed after segmenting image by
maximum distance method and fusing spatial and spectral information. Finally, an statistical dispersion of infrared
multispectral images(SDOIMI) recognition criteria is formulated in terms of spectral radiation difference of interesting
targets. In simulation, nine sub-bands multispectral images of real ship target and shipborne aerosol infrared decoy
modulated by laser simulating ship geometry appearance are obtained via using spectral radiation curves. Digital
simulation experiment result verifies that the algorithm is effective and feasible.
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