Xiyang Zhi

Modeling of image matching accuracy with image metrics based on least squares matching algorithm

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.

Modeling and validation of spectral BRDF on material surface of space target

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.

Fast small target tracking in IR imagery based on improved similarity measure

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.

Degradation and restoration of high resolution TDICCD imagery due to satellite vibrations

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.

A method on lightweight for the primary mirror of large space-based telescope based on neural network

With the aperture of telescope becoming larger, the mass of primary mirror and other relevant structures will become heavier as well. Therefore, lighting weight for large space-based telescope is necessary. This paper purposed a method based on Neural Network aims to build a math model for primary mirror of large space-based telescope, which can reduce weight of the telescope and smaller mirror deformation caused by gravity release effectively. In the meantime, it can also improve stiffness of structure and reduce thermal strain caused by on orbit temperature variation effectively. The model describes the relationship between the structure of primary mirror of large space-based telescope and corresponding deformation, and describes the optical performance of mirror by using Zernike Polynomial. To optimize the structure of primary mirror lightweight, we take the deformation of mirror and its optical performance into consideration. To apply the structures parameters and its corresponding deformations to Neural Network training, we use the combination samples of different mirror lightweight structure parameters and corresponding deformation which caused by gravity release and thermal condition. Finally, by taking advantage of the Neural Network model to optimize the primary mirror lightweight of 1-meter rectangle space-based telescope, which can make the RMS 0.024λ (λ=632.8nm)and areal density under 15kg/m2. This method combines existing results and numerical simulation to establish numerical model based on Neural Network method. Research results can be applied to same processes of designing, analyzing, and processing of large space-based telescope directly.

Identification method of satellite local components based on combined feature metrics

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.