Shadow Removal of Hyperspectral Remote Sensing Images With Multiexposure Fusion
Puhong Duan, Shangsong Hu, Xudong Kang, Shutao Li
Abstract
Shadow removal is a challenging problem in hyperspectral remote sensing images due to its spatial-variant properties and diverse patterns. In this work, a shadow removal framework with multiexposure fusion is proposed for hyperspectral remote sensing images, which consists of three major steps. First, a color space conversion method is exploited to detect the shadow regions. Second, the principle of the intrinsic decomposition model is utilized to generate a set of differently exposed hyperspectral images (HSIs), i.e., multiexposure images. Third, the generated multiexposure images and the original HSIs are fused together with a two-stage image fusion method so as to remove the shadows in hyperspectral remote sensing images effectively. Experiments performed on three real hyperspectral datasets confirm that the performance of the proposed method outperforms other state-of-the-art shadow removal approaches.