Level set hyperspectral image classification using best band analysis

We present a supervised hyperspectral classification procedure consisting of an initial distance-based segmentation method that uses best band analysis (BBA), followed by a level set enhancement that forces localized region homogeneity. The proposed method is tested on two hyperspectral images of an urban and rural nature. The proposed method is compared to the maximum likelihood (ML) method using BBA. Quantitative results are compared using segmentation and classification accuracies. Results show that both the initial classification using BBA features and the level set enhancement produced high-quality ground cover maps and outperformed the ML method, as well as previous studies by the authors. For example, with the compact airborne spectrographic imager image, the ML method resulted in accuracies <= 95.5%, whereas the level set segmentation approach resulted in accuracies as high as 99.7%.