Band dual density discrimination analysis for hyperspectral image classification

A novel band discrimination analysis framework for hyperspectral image (HSI) supervised classification is proposed based on dual density (DD). Different from the popular supervised band selection (BS) approaches which measure the discrimination among classes under multivariate normal distribution hypothesis, our work infers the class discrimination degree (overlapping extent) for valid extraction of band subset without any assumed distribution. In the proposed framework, it is crucial to find indexes to measure the discrimination degree of each band, and therefore we develop the DD indexes, including the homogeneity density and the heterogeneity density. Viewing each band of the HSI as a data set, i.e., the data points in each data set are 1-D, and we first obtain the DD value pairs for all data points in each data set. Then, for each data set, we determine its discrimination degree using DD-based zone ratio or score quantify strategy. Finally, the bands, which are determined as the nonoverlapped or have high scores, are chosen as the band subset for the subsequent classification. Superiorities of the proposed BS are demonstrated on the three real-world HSIs over several well-known BS algorithms in terms of classification accuracy and speed.