Hyperspectral Band Selection for Multispectral Image Classification with Convolutional Networks

TL;DR

This paper introduces a two-step band selection method combining filter and wrapper techniques with CNN evaluation to optimize spectral bands for hyperspectral image classification, improving multispectral sensor design.

Contribution

A novel hybrid band selection approach that reduces spectral redundancy and enhances classification performance using CNN-based evaluation.

Findings

Outperforms existing feature selection methods on hyperspectral datasets.

Effectively reduces spectral bands while maintaining high classification accuracy.

Simulates multispectral sensor design with improved spectral band choices.

Abstract

In recent years, Hyperspectral Imaging (HSI) has become a powerful source for reliable data in applications such as remote sensing, agriculture, and biomedicine. However, hyperspectral images are highly data-dense and often benefit from methods to reduce the number of spectral bands while retaining the most useful information for a specific application. We propose a novel band selection method to select a reduced set of wavelengths, obtained from an HSI system in the context of image classification. Our approach consists of two main steps: the first utilizes a filter-based approach to find relevant spectral bands based on a collinearity analysis between a band and its neighbors. This analysis helps to remove redundant bands and dramatically reduces the search space. The second step applies a wrapper-based approach to select bands from the reduced set based on their information entropy values, and trains a compact Convolutional Neural Network (CNN) to evaluate the performance of the current selection. We present classification results obtained from our method and compare them to other feature selection methods on two hyperspectral image datasets. Additionally, we use the original hyperspectral data cube to simulate the process of using actual filters in a multispectral imager. We show that our method produces more suitable results for a multispectral sensor design.