The optical image has high resolution, but it is vulnerable to the adverse environment, resulting in the loss of spectral details. SAR image has strong penetrating power to vegetation, cloud and snow, but it will be interfered by speckle noise. The complementarity of the two images can effectively overcome the limitations of a single image in a complex environment. However, optical image and SAR image have different imaging mechanisms and different gray information, which may lead to the failure of the performance of the two images registration. In order to solve the above problem, in this paper, we propose a method of optical image and SAR image registration based on position constraint. First, the traditional SIFT algorithm is used to register the image coarsely, and then the position of the feature descriptor is locally optimized through the spatial geometric structure characteristics between similar feature points. Experimental results have shown the effectiveness of the proposed method.
Although optical image has high resolution, it is extremely susceptible to the impact of harsh environments which will cause the loss of spectral details. SAR image has strong penetrating power to vegetation, clouds and snow, but they will be disturbed by coherent speckle noise. In addition, the imaging mechanism of optical image and SAR image is different, and the gray information is vastly different, which may lead to the failure of the performance of the two image registrations. In this letter, we propose an improved SIFT-Like image registration algorithm. Different gradient operators are used to calculate the gradient of optical image and SAR image respectively, and the corresponding scale space is constructed, and then the feature descriptor is constructed. Experimental results on optical and SAR image pairs show that the proposed algorithm in this letter has a significant improvement in the correct matching rate and registration accuracy compared with other algorithms.
With the continuous development of remote sensing technology, the types of remote sensing image data are more diversified. More spatial information of images can be obtained by multisource fusion. In addition, the complementarity between sensors can effectively overcome the limitations of a single sensor in complex environments. The registration of optical image and SAR image is the key point in multisource image registration. Optical images have high resolution. But are vulnerable to the impact of harsh environments, resulting in the loss of spectral details. SAR images have strong penetrability to vegetation, cloud, ice and snow. But they are interfered by speckle noise. The imaging mechanism of optical image and SAR image is different, and the difference of gray information is large, which may lead to the performance failure of two kinds of image registration. To solve the above problems, in this paper, we propose a method of optical image and SAR image registration based on geometric constraints, which optimizes the feature descriptor locally through the spatial geometric structure characteristics between similar feature points. The experimental results show that the proposed method improves the matching performance compared with several state-of-the-art methods in terms of the matching accuracy.
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