Discovering Diverse Subset for Unsupervised Hyperspectral Band Selection

Abstract

Band selection, as a special case of the feature selection problem, tries to remove redundant bands and select a few important bands to represent the whole image cube. This has attracted much attention, since the selected bands provide discriminative information for further applications and reduce the computational burden. Though hyperspectral band selection has gained rapid development in recent years, it is still a challenging task because of the following requirements: 1) an effective model can capture the underlying relations between different high-dimensional spectral bands; 2) a fast and robust measure function can adapt to general hyperspectral tasks; and 3) an efficient search strategy can find the desired selected bands in reasonable computational time. To satisfy these requirements, a multigraph determinantal point process (MDPP) model is proposed to capture the full structure between different bands and efficiently find the optimal band subset in extensive hyperspectral applications. There are three main contributions: 1) graphical model is naturally transferred to address band selection problem by the proposed MDPP; 2) multiple graphs are designed to capture the intrinsic relationships between hyperspectral bands; and 3) mixture DPP is proposed to model the multiple dependencies in the proposed multiple graphs, and offers an efficient search strategy to select the optimal bands. To verify the superiority of the proposed method, experiments have been conducted on three hyperspectral applications, such as hyperspectral classification, anomaly detection, and target detection. The reliability of the proposed method in generic hyperspectral tasks is experimentally proved on four real-world hyperspectral data sets.

Optimal Clustering Framework for Hyperspectral Band Selection

“…According to the employed searching strategy [24], unsupervised band selection can be categorized into rankingbased, clustering-based, greedy-based and evolutionary-based methods. Ranking-based methods assign each band a rank value and simply select the top-rank bands with the desired number.…”

Band Subset Selection for Hyperspectral Image Classification

“…It is completely different from existing BS methods, with the following contributions: (i) It is a BSS method particularly developed for HSIC; (ii) It is quite different from single band-constrained methods in [26] and multiple-band constrained methods in [68], by constraining multiple class signature vectors instead of multiple bands; (iii) It develops three numerical search algorithms to find optimal band subsets which are different from the graph-based approaches [40,43] used by other SMMBS methods; (iv) It is very simple to implement via (7) with no parameters needing to be tuned; (v) Most importantly, it shows that HSIC can be improved by BS provided that the number n BS of selected bands and the set of n BS bands are properly selected.…”

“…One is to use graph-based representations with each path used to specify a particular band subset. For example, Yuan et al [43] proposed a graph-based SMMBS method, called multigraph determinantal point process (MDPP), which makes use of multiple graphs to discover a structure and diverse band subset from a graph where each node represents a band and the edges are specified by similarity between bands. Accordingly, a path represents a possible band subset.…”

Spatial-Spectral Multiple Manifold Discriminant Analysis for Dimensionality Reduction of Hyperspectral Imagery

“…However, the sensors with higher resolution produce a very large volume of data, it will render the traditional image processing algorithms designed for multispectral imagery ineffective [6,7]. In particular, the high dimensionality of HSI data brings about the "curse of dimensionality" problem, that is, under a fixed, small number of training samples, the classification accuracy of HSI data decreases when the dimensionality of HSI data increases [8][9][10]. Besides, the spectral bands are highly correlated and some spectral bands may not carry discriminant information in a specific application.…”