Top-Down Visual Saliency Detection in Optical Satellite Images Based on Local Adaptive Regression Kernel

Abstract

This paper proposes a novel top-down visual saliency detection method for optical satellite images using local adaptive regression kernels. This method provides a saliency map by measuring the likeness of image patches to a given single template image. The local adaptive regression kernel (LARK) is used as a descriptor to extract feature and compare against analogous feature from the target image. A multi-scale pyramid of the target image is constructed to cope with large-scale variations. In addition, accounting for rotation variations, the histogram of kernel orientation is employed to estimate the rotation angle of image patch, and then comparison is performed after rotating the patch by the estimated angle. Moreover, we use the bounded partial correlation (BPC) to compare features between image patches and the template so as to rapidly generate the saliency map. Experiments were performed in optical satellite images to find airplanes, and experimental results demonstrate that the proposed method is effective and robust in complex scenes.