Deakin University
Browse

Hyperspectral Image Classification via Information Theoretic Dimension Reduction

Version 2 2024-06-02, 15:12
Version 1 2023-03-21, 02:11
journal contribution
posted on 2024-06-02, 15:12 authored by Md Rashedul IslamMd Rashedul Islam, A Siddiqa, M Ibn Afjal, MD PALASH UDDINMD PALASH UDDIN, A Ulhaq
Hyperspectral images (HSIs) are one of the most successfully used tools for precisely and potentially detecting key ground surfaces, vegetation, and minerals. HSIs contain a large amount of information about the ground scene; therefore, object classification becomes the most difficult task for such a high-dimensional HSI data cube. Additionally, the HSI’s spectral bands exhibit a high correlation, and a large amount of spectral data creates high dimensionality issues as well. Dimensionality reduction is, therefore, a crucial step in the HSI classification pipeline. In order to identify a pertinent subset of features for effective HSI classification, this study proposes a dimension reduction method that combines feature extraction and feature selection. In particular, we exploited the widely used denoising method minimum noise fraction (MNF) for feature extraction and an information theoretic-based strategy, cross-cumulative residual entropy (CCRE), for feature selection. Using the normalized CCRE, minimum redundancy maximum relevance (mRMR)-driven feature selection criteria were used to enhance the quality of the selected feature. To assess the effectiveness of the extracted features’ subsets, the kernel support vector machine (KSVM) classifier was applied to three publicly available HSIs. The experimental findings manifest a discernible improvement in classification accuracy and the qualities of the selected features. Specifically, the proposed method outperforms the traditional methods investigated, with overall classification accuracies on Indian Pines, Washington DC Mall, and Pavia University HSIs of 97.44%, 99.71%, and 98.35%, respectively.

History

Journal

Remote Sensing

Volume

15

Article number

ARTN 1147

Pagination

1147-1147

Location

Basel, Switzerland

ISSN

2072-4292

eISSN

2072-4292

Language

eng

Publication classification

C1 Refereed article in a scholarly journal

Issue

4

Publisher

MDPI