Level set hyperspectral image segmentation using spectral information divergence-based best band selection

We present a supervised hyperspectral segmentation procedure, consisting of best band analysis (BBA), an initial distance-based segmentation, and level set segmentation enhancement by forcing localized vicinities to be more homogeneous. BBA uses the spectral information divergence (SID) to reduce each pixel’s high dimensional data to a scalar value, where the Bhattacharyya Distance (BD) is maximized. The initial segmentation is based on feature vectors created from the SID metric. The level set segmentation then enhances areas that do not have spatially homogeneous ground cover. The proposed method is tested on a 72-band Compact Airborne Spectrographic Imager (CASI) image of a farm area in northern Mississippi, U.S.A. The proposed method is compared to a BBA-based maximum-likelihood (ML) method. 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. The ML method resulted in accuracies ≥95.5%, whereas the level set segmentation approach resulted in accuracies as high as 99.7%.