Shadow invariant classification for high-resolution images

Abstract

Shadows are one of the most commonly observed optical phenomena in real-world environments. Often they are encountered in a target-scene area depending on the angle of view, scene structure, scene illumination, and scene geometry. In practice, the performance of several image-driven analysis techniques, such as classification tasks, becomes unstable in the presence of shadows. Specifically, shadow regions are profoundly difficult for image classification applications resulting in false predictions because they are inherently complex and are quite unlike anything seen during training. This paper introduces a novel algorithm named shadow invariant classifier system (SICS) to mitigate the challenges posed by shadows in various classification tasks, as such studies are currently lacking. Also, we propose a general framework for automatically selecting an optimal threshold for the proposed similarity matching methods (SMMs). The proposed algorithms use spectral information alone and require no pre-knowledge of shadows and their environmental settings. They are resilient to the complexities associated with the presence of shadows. To validate the performance, we conducted several classification experiments using six different remote sensing images from multi-sensor and multiplatform modalities having a varying scale of shadows and lighting effects. A thorough evaluation using the high spatial, spectral resolution multispectral and hyperspectral imagery demonstrates the effective shadow insensitive classification performance obtained by the proposed algorithms