Significant radiometric differences and weak grayscale correlations exist between optical and SAR images. As a result, there are severe spectral and spatial distortions in the fused images. We propose a fusion method of optical and SAR remote sensing images that couples the gain injection method and the guided filter. The proposed method is based on the fusion framework of generalized intensity-hue-saturation non-subsampled contourlet transform, and the gain injection is used for the low-frequency coefficient fusion to reduce the spectral distortion. Then, the divergence is used as the activity measure operator to calculate the initial weight template for the high-frequency coefficients. The guided filter is used to optimize the edge details of the initial weight template. The fused high-frequency coefficients are obtained by weighted average. Through comparison experiments with existing fusion methods, the results show that the proposed method has the best quality of fusion and the proposed method has the best performance.
High-resolution multisource optical remote sensing images often have considerable non-linear radiation and scale differences that become more prominent as the image resolution increases. Since this problem may cause low registration accuracy, we propose an automatic registration algorithm based on point–line spatial geometric information (PLSGI). First, we propose an improved scale invariant feature transform algorithm using the quadtree uniformization, the Bhattacharyya distance, and the slope constraint to obtain many correct point pairs. Then we generate a new global line descriptor using PLSGI. In particular, the descriptor can resist the non-linear radiation and scale differences of multisource remote sensing images. Finally, we use a piecewise linear model to warp the sensed images based on a set of tie points that consist of corresponding features and corresponding intersections. The effectiveness of the proposed algorithm was validated using multiple groups of high-resolution remote sensing images. The experimental results indicated that our algorithm is highly generalized and robust for high-resolution multisource remotely sensed images.
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