Matrix-Based Margin-Maximization Band Selection With Data-Driven Diversity for Hyperspectral Image Classification

For hyperspectral image classification, high-dimensional spectral features not only increase the computational and storage burden but also degrade the classification accuracy due to the Hughes phenomenon. Band selection is an important technique to solve these issues without destroying the interpretation of data. This paper presents a matrix-based margin-maximization method of band selection with data-driven diversity. In particular, the matrices composed of adjacent pixels in space are fed to the hinge loss function with a row-sparse constraint. This constraint is used to select the expected bands and preserve the spatial structure information simultaneously while maximizing the margin between the classes. In consideration of the continuity of bands in the spectral dimension, a novel regularization term that is continually updated according to the current context is added to promote the differential expression of dissimilar bands. Finally, the one-against-all parallel mechanism is used to learn a coefficient matrix for each class, and class-related bands are then carefully selected by a partitioning strategy (e.g., k-means clustering) based on the learned coefficient matrix. Experiments are conducted on three hyperspectral data sets and four widely used classifiers. The experimental results have shown that our proposed method is superior to several state-of-the-art methods, especially when the number of selected bands is relatively small.