Hybrid Spectral Unmixing in Land-Cover Classification

Identifying land-cover and specifically the type of the material that constitutes building roofs in urban areas provides important reference information for later procedures including semantic labeling, bridge masking, and 3D reconstruction. In this paper, we present a hybrid unmixing-based classification framework that integrates both class-wise unsupervised unmixing and supervised unmixing that effectively convert the classification problem from the original spectral space to the abundance space, such that the intrinsic characteristics of each material can be better represented. Experimental results demonstrate competitive performance in terms of classification accuracy. In addition, we show that the proposed approach has the capability of handling new region of interest with similar scene content but different illumination geometry and atmospheric composition, which is crucial in classification of satellite images with a limited amount of training data.