Cooperative spectral and spatial feature fusion for camouflaged target detection

Abstract

This paper presents a novel camouflaged target detection method using spectral and spatial feature fusion. Conventional unsupervised learning methods using spectral information only can be feasible solutions. Such approaches, however, sometimes produce incorrect detection results because spatial information is not considered. This paper proposes a novel band feature selection method by considering both the spectral distance and spatial statistics after spectral normalization for illumination invariance. The statistical distance metric can generate candidate feature bands and further analysis of the spatial grouping property can trim the useless feature bands. Camouflaged targets can be detected better with less computational complexity by the spectral-spatial feature fusion.