Random cross-observation intensity consistency method for large-scale SAR images mosaics: An example of Gaofen-3 SAR images covering China

Abstract

Abstract Large-scale microwave remote sensing research requires multiple synthetic aperture radar (SAR) image mosaics. However, owing to multiple factors, different SAR images frequently exhibit intensity differences. This leads to poor intensity continuity after mosaicking, making SAR image interpretation extremely difficult. The existing method assumes that SAR images have been subject to strict radiometric calibration and mainly focuses on intensity balancing to minimize the intensity difference between adjacent images. This method is not effective when the intensity difference between SAR images is large. In this paper, a novel random cross-observation intensity consistency (RCOIC) algorithm is proposed to eliminate the intensity differences for SAR image mosaics. The proposed algorithm is based on global intensity constraints. First, source images are downsampled. Then, the gain correction coefficients of each image in a study area are accurately estimated through random cross-observation. Further, multi-view local intensity correction of each image in the study area is performed using the overlapping areas of adjacent images, and a low-resolution intensity reference map covering the study area is generated. Finally, the intensities of high-resolution source images are corrected by constructing local independent linear models of images based on the low-resolution intensity reference map. Gaofen-3 satellite images with large ranges and large time differences, covering most of China s land area, were selected to test the effect of the newly developed algorithm. After processing, the overall visual effect was good after mosaicking, the intensity transitions at the edges of the images were smooth, the intensity distributions corresponded better to actual ground objects, and the statistical result of adjacent images overlapping regions was good. This validates the reliability of the algorithm.