Hyperspectral image (HSI) usually holds information of land cover classes as a set of many contiguous narrow spectral wavelength bands. For its efficient thematic mapping or classification, band (feature) reduction strategies through Feature Extraction (FE) and/or Feature Selection (FS) methods for finding the intrinsic bands’ information are typically applied. Principal Component Analysis (PCA) is a frequently employed unsupervised linear FE method whereas cumulative-variance accumulation is used for selecting top features from PCA data. However, PCA can fail to extract intrinsic structure of HSI due to global variance consideration and domination by visible and near infrared bands while cumulative-variance accumulation has no capability to exploit non-linear relationships among the transformed features produced by PCA-based FE methods. Consequently, we propose an information theoretic normalized Mutual Information (nMI)-based minimum Redundancy Maximum Relevance (mRMR) non-linear measure to select the intrinsic features from the transformed space of our previously proposed Segmented-Folded-PCA (Seg-Fol-PCA) and Spectrally Segmented-Folded-PCA (SSeg-Fol-PCA) FE methods. We extensively analyse the effectiveness of the proposed unsupervised FE and supervised FS combinations Seg-Fol-PCA-mRMR and SSeg-Fol-PCA-mRMR with that of PCA-based existing linear and non-linear state-of-the-art methods. In addition, cumulative variance-based top features pick-up strategy is considered with all FE methods and Renyi quadratic entropy-based FS is used with Kernel Entropy Component Analysis (Ker-ECA). The experimental results illustrate that SSeg-Fol-PCA-mRMR and Seg-Fol-PCA-mRMR obtain highest classification result e.g. 95.39% and 95.03% respectively for agricultural Indian Pines HSI, and 96.58% and 95.30% respectively for urban Washington DC Mall HSI while the classification accuracies using all original features of the HSIs are 70.28% and 91.90% respectively. Moreover, the proposed SSeg-Fol-PCA-mRMR and Seg-Fol-PCA-mRMR outperform all investigated combinations of FE and FS using the real HSI datasets.

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.

Hyperspectral images (HSI) contain a wide range of information, the most prominent technology for observing the earth. However, using an original HSI high-dimensional datacube, the classification task faces significant challenges since it has a high computational cost. As a result, dimensionality reduction is indispensable. A dimension reduction method has been introduced in this paper, including feature extraction and feature selection to obtain feature subsets. Minimum Noise Fraction (MNF) is a popular feature extraction method for HSI, requiring a high computational capability. We propose a segmented MNF that divides the complete HSI into groups utilising normalised cross-cumulative residual entropy (nCCRE). An nCCRE-based feature selection is also employed to improve the quality of the chosen features using the max-relevancy min-redundancy measure. The support vector machine (SVM) classifier is used on two real HSI to evaluate the efficiency of the extracted subsets.

Hyperspectral images (HSIs) contain notable information of land objects by acquiring an immense set of narrow and contiguous spectral bands. Feature Extraction (FE) and Feature Selection (FS) as dimensionality (band) reduction strategies are performed to enhance the classification result of HSI. PCA is frequently exploited for the FE of HSI. However, it often possesses the inability to extract local and subtle HSI structures. As such, segmented-PCA (SPCA), spectrally segmented-PCA (SSPCA) and folded-PCA (FPCA) are presented for local and useful FE from the HSI. In this paper, we propose two FE methods called segmented-FPCA (SFPCA) and spectrally segmented-FPCA (SSFPCA). SFPCA exploits SPCA and FPCA while SSFPCA exploits SSPCA and FPCA together. In particular, SFPCA and SSFPCA apply FPCA on highly correlated and spectrally grouped HSI bands respectively. We consider nonlinear methods Kernel-PCA (KPCA) and Kernel Entropy Component Analysis (KECA) for extended comparison. For the experimented agricultural Indian Pine and urban Washington DC Mall HSIs, the results manifest that SFPCA (95.6262% for the agricultural HSI and 97.4782% for the urban HSI) and SSFPCA (96.3221% for the agricultural HSI and 98.0116% for the urban HSI) outperform the conventional methods.