Hyperspectral Image Classification via Information Theoretic Dimension Reduction

Abstract

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.

Spectrally Segmented-Enhanced Neural Network for Precise Land Cover Object Classification in Hyperspectral Imagery

“…The proposed model exhibits consistent performance across the three datasets, regardless of the proportion of training samples used. To validate the proposed classification algorithm, we compare it with several other algorithms, namely, SVM [11], 2D-CNN [25], 3D-CNN [26], Fast 3D-CNN [42], HybridSN [29], SpectralNET [30], and TRI-CNN [35]. The quantitative comparisons of the methods being compared are presented in Tables 7-9, with the best results highlighted in bold.…”

Detailed Land Use Classification in a Rare Earth Mining Area Using Hyperspectral Remote Sensing Data for Sustainable Agricultural Development

“…To address this situation, many researchers have used different feature preference methods for dimensionality reduction, such as Mahdianpari et al [44] who used the J-M distance to quantitatively analyze the separability of different types of wetlands under different features; they then classified the land classes after feature selection based on the separability results, which resulted in robust classification accuracy. Md Rashedul et al [45] combined MMF and mRMR methods for the dimensionality reduction in feature variables. Hao Yufeng et al [46] and Fushuyu et al [47] performed variable optimization for wetland classification through the Relief-F algorithm and RFE algorithm, respectively.…”

Mapping Alteration Minerals Using ZY-1 02D Hyperspectral Remote Sensing Data in Coalbed Methane Enrichment Areas

“…Satellite remote sensing technology possesses the capability to detect and analyze surface rock and mineral composition with precision [5][6][7][8][9][10]. Specifically, hyperspectral data provide high-resolution images for ground object classification and enable detailed analysis of the chemical composition of certain minerals [11][12][13][14][15]. In recent years, hyperspectral remote sensing technology has played an important role in mineral identification [16][17][18], geological mapping [19], alteration anomaly zoning [20,21], and prospecting prediction [22,23].…”