Geometric-correction is used to correct the distortion between image and corresponding ground target, it is one of the most time-consuming steps in remote sensing data processing, thus becoming a bottleneck restricting the effectiveness of remote sensing data. Therefore, a fast geometric-correction method of remote sensing image, based on GPU parallel processing, is proposed in this paper. First, a three-dimensional (3D) linear transformation is introduced to reduce the computational amount of coordinate projection conversion. Then, the GPU parallel mapping is conducted. Furthermore, two performance optimization methods are used to further improve the efficiency. The approach is tested with GF-1 panchromatic image with Intel i7 CPU and NVIDIA Tesla M2070 GPU. The experimental results show that the processing time is only 2.08 s, with a speedup ratio of 142.42, which could meet the requirement of fast geometric-correction for big-data remote sensing images.
The camera mounted on the satellite two-dimensional tracking platform takes images of the star background, and using the star map recognition method based on triangle matching can eliminate stars and extract non-stellar targets. When the tracking platform is used to track the non-stellar targets and is moving fast, the tracking targets will be lost. To solve this problem, a spatial target tracking method based on stellar background is proposed in this paper. Firstly, the rotation matrix of the camera in the geocentric equatorial inertial coordinate system at the moment of two consecutive image frames is calculated based on triangle matching, and targets of each image frame are extracted. For a certain target, the coordinate position corresponding to the target are calculated based on the transformation of the rotation matrix. And then the motion of the target in the inertial coordinate system is superimposed to derive the predicted coordinate position of the target. Finally, the nearest Euclidean distance from the predicted coordinates is found among the current target points. If the Euclidean distance is less than a certain threshold value, the target tracking is successful. The simulation experiment shows that when the tracking platform is moving fast, the target can be tracked continuously and stably.
Loss of texture information and color distortion have always been two key issues that plague the quality of fusion images. In recent years, with the development of remote sensing payload technology, the difference between the spectral range of panchromatic and multi-spectral images has become larger and larger, resulting in the problems proposed above becoming more prominent in the process of true color fusion. On one hand, the energy distribution of water areas and vegetation in the near-infrared and visible spectral ranges is very different, therefore, the color distortion is mainly concentrated in the water area and vegetation area, the specific manifestation is that the energy of the vegetation is sufficient and the energy of the water area is very small. On the other hand, multi-spectral devices have poor antisaturation and anti-dispersion characteristics, which often leads to the loss of texture information in images. In addition, the lack of energy of multi-spectral sensor results in limited recognition of textures in the shadow area of the fusion image. Based on the analysis of the shortcomings of the existing fusion methods, we propose a pan-sharpening fusion optimization method based on the pyramid model in this paper. This method first uses the spectral relationship between the spectral image and the panchromatic image to build the basic fusion model, then, in order to prevent the "illconditioned equation" phenomenon appeared during the fusion process, unequal conditional equations are introduced into the basic fusion model to form simultaneous equations to avoid color distortion and invalid data in the fusion results. Secondly, in order to suppress the blurring of the edges of the fused image caused by the saturation overflow in the image, we calculates the ratio of the panchromatic image to the up-sampling multi-spectral images, and replaces the deficiency of the previous fusion model to generate fusion images with high clarity and high spectral fidelity.
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