Hyperspectral Band Selection Based on Adaptive Neighborhood Grouping and Local Structure Correlation

Xin Wang,Guoqiang Wang,Carlos Michel

doi:10.1155/2021/5530385

Abstract

Band selection is a direct and effective dimension reduction method and is one of the hotspots in hyperspectral remote sensing research. However, most of the methods ignore the orderliness and correlation of the selected bands and construct band subsets only according to the number of clustering centers desired by band sequencing. To address this issue, this article proposes a band selection method based on adaptive neighborhood grouping and local structure correlation (ANG-LSC). An adaptive subspace method is adopted to segment hyperspectral image cubes in space to avoid obtaining highly correlated subsets. Then, the product of local density and distance factor is utilized to sort each band and select the desired cluster center number. Finally, through the information entropy and correlation analysis of bands in different clusters, the most representative bands are selected from each cluster. Regarding evaluating the effectiveness of the proposed method, comparative experiments with the state-of-the-art methods are conducted on three public hyperspectral datasets. Experimental results demonstrate the superiority and robustness of ANG-LSC.

Highlights

Hyperspectral images (HSI), as a rich spectral information source, can accurately describe objects and are widely used in various fields such as marine exploration, military target detection, forestry, and hydrology [1,2,3,4]
The feature selection method [6] reduces the dimensionality of HSI by finding the most representative band to form a data subset, i.e., a group of most important bands is selected from all spectral bands to represent the entire spectrum
Many clustering-based band selection methods have been proposed, but most of them only take into account redundancy between bands, neglecting the amount of information in the subset of selected bands

Summary

Introduction

Hyperspectral images (HSI), as a rich spectral information source, can accurately describe objects and are widely used in various fields such as marine exploration, military target detection, forestry, and hydrology [1,2,3,4]. Feature extraction and feature selection ( known as “band selection”) are two of the most widely used dimensionality reduction strategies. The feature selection method [6] reduces the dimensionality of HSI by finding the most representative band to form a data subset, i.e., a group of most important bands is selected from all spectral bands to represent the entire spectrum. This way preserves the physical meaning of the original spectral data and facilitates the interpretation of the selected datasets. The band selection method is very suitable for dimensionality reduction of hyperspectral data

Methods

Results

Conclusion