An Efficient Subspace Partition Method Using Curve Fitting for Hyperspectral Band Selection

Band selection is a valid method to reduce redundant information in hyperspectral images (HSIs). Typically, two methods are used to select representative bands: ranking bands based on predefined criteria and selecting cluster centers by grouping bands. It is advantageous to combine these two methods for hyperspectral band selection tasks since their benefits are complimentary. To take full of these advantages, we propose a hyperspectral band selection method using curve-fitting subspace partition (CFSP), including the subspace partition method and local context representative band selection method. The contributions of this letter are summarized below: 1) through fitting the spectral curves and then utilizing the point of maximum curvature to partition the band set, the similar and adjacent bands can be divided into the same group, which is very consistent with the way of subspace partition and 2) a representative band selection method in the local context is proposed. The locally optimal bands are selected sequentially to constitute the candidate band set. Then, through ranking and iteratively updating the candidate band set, we can effectively find the desired bands. The experiments on three public HSI datasets show that the proposed method has significant advantages compared with some advanced competitors. In particular, on the Salinas dataset, the selected bands achieved an excellent average overall accuracy (OA) of 91.32% using the support vector machine (SVM) classifier.