An adaptive two-scale enhancement method to visualize man-made objects in very high resolution SAR images

Abstract

Ocean vessels and aircrafts usually cause large oscillated responses and span a high dynamic range (HDR) in synthetic aperture radar (SAR) images. To visualize and further recognize these objects on common display devices, it is necessary to compress the HDR at the proper ratio and enhance the details at both low and high grey levels. To achieve this goal, an adaptive two-scale enhancement method is proposed. First, to enhance the details at low grey levels, an adaptive global HDR compression algorithm is designed based on the grey-level distribution feature. Then, with a simplified heterogeneity measurement based on the weighted standard deviation and entropy at a local scale, high-backscattering objects are determined and enhanced to highlight their peak details at high grey levels. The proposed method is validated using two different types of HDR SAR images, and the experimental results suggest that the proposed method visualizes the information at all grey levels and enhances the object’s local peaks. Compared with gamma mapping algorithms, which are widely used for SAR image display, the proposed method outperforms in visualizing the details of man-made objects in very high resolution SAR images.