Segmentation-based linear discriminant analysis with information theoretic feature selection for hyperspectral image classification

The use of hyperspectral imaging sensors has greatly improved the classification of remotely sensed data because of the abundant spectral information they offer. However, the numerous contiguous, tiny wavelength bands captured in hyperspectral images (HSIs) often hinder the classification process. To overcome the aforementioned problem, various feature reduction techniques, including feature extraction (FE) and feature selection (FS), are commonly employed to improve classification performance. Linear discriminant analysis (LDA) is a well-established approach that has been utilized for the FE of HSI. LDA's consideration of global characteristics and the variance accumulator for FS can lead to the poor reduction of HIS's intrinsic characteristics. Furthermore, LDA's limited ability to select a very low number of features, i.e. the number of classes minus one, restricts its effectiveness in HSI classification. Therefore, we introduce an FS method based on a non-linear information-theoretic measure, normalized mutual information (nMI) combined with minimum redundancy maximum relevance (mRMR). This approach is used to identify inherent features from the transformed space of our proposed correlation-based segmented-LDA (SLDA) and spectral region-based segmented-LDA (SSLDA) FE methods. We thoroughly compare the performance of the SLDA-mRMR and SSLDA-mRMR methods with the existing linear and non-linear state-of-the-art techniques, including unsupervised principal component analysis (PCA), PCA-based methods, supervised LDA, and LDA-based methods. Additionally, we explore the performance of nMI-based mRMR selection with all FE methods by incorporating a cumulative variance-based top features pick-up strategy. Based on the experimental results, we observe that SSLDA-mRMR and SLDA-mRMR achieve the highest classification accuracy, such as 91.91%, and 91.57% for agricultural Indian Pines, respectively, 97.66%, and 97.54%, respectively, for Kennedy Space Center, and 96.69%, and 96.57%, respectively, for Pavia University. In contrast, the classification accuracies using all original features of the HSIs are 71.39%, 70.01%, and 83.52%, respectively. Moreover, our proposed SSLDA-mRMR and SLDA-mRMR combinations outperformed all other FE and FS combinations that were examined using actual HSI datasets.