An adaptive SAR image enhancement method is presented for reducing the speckle noise and increasing the contrast of
synthetic aperture radar (SAR) images. First, a fuzzy logic based filter, employing fuzzy edge to weight the contributions
of pixel values in filter window, is used to filter the speckles. Second, the original SAR image is decomposed into lowfrequency
component and high-frequency component. The fuzzy filtered image is viewed as the low-frequency
component, and the contrast limited adaptive histogram equalization algorithm is used to increase its contrast. The highfrequency
component is obtained by subtracting the low-frequency component from the original image, and its gain is
controlled by fuzzy structural which employed to express the degree of a pixel belonging to structures. After processed
one after the other, the two components are added together to form the final enhanced SAR image. Experimental results
show the excellent effect of the proposed method by visual observation and numerical measurement. Many fine
structures and little speckle noise can be seen from the enhanced SAR images.
A novel 3D terrain matching algorithm is presented in this paper. A terrain feature vector map (FVM), composed of local mean and local gradient, is employed to represent the terrain elevation map (TEM). Compared with traditional matching algorithm using the magnitude of gradient to match, the new algorithm uses each component of the gradient vector to match individually, and it is able to generate two interim matching positions. Different from traditional matching algorithms which usually estimate an optimum matching position under some criterions at the end, the new algorithm fused the two interim matching positions to generate a final matching position or refuse to position in order to increase the matching confidence, which is very important because it is hardly acceptable to employ a mismatched position to correct the error of Inertial Navigation System (INS). Due to the stability of terrain and the high-precision of lidar ranging, the mean of a sensed terrain elevation map (STEM) sized terrain is quite stable. So it is bestowed to accelerate the matching process and to reduce mismatches at different terrain heights. Compared with other mismatch-eliminated methods based on neural network (NN) or support vector machine (SVM), the new method do not need training samples and is more stable and robust. Experimental results show that the proposed algorithm is effective and robust.