A novel strategy for classifying spectral-spatial shallow and deep hyperspectral image features using 1D-EWT and 3D-CNN.

Hyperspectral images (HIs) are used in diverse disciplines, such as resource handling, land cover analysis, food science, anomaly detection, and precision agriculture. Researchers have been working on a number of visual processing and machine intelligence algorithms to handle this type of data as efficiently as feasible. Deep learning approaches have advanced significantly in the field of machine vision, which is also having a big impact on the analysis of hyperspectral data. To increase its discriminative potential for HI classification, this work suggests a powerful 3D-CNNs (Convolutional Neural Networks) architecture, where the shallow features extracted using 1D-EWT (Empirical Wavelet Transform) are served as input, and the ultimate output of the CNN are projected class-related outcomes. The framework is known as PEC, where P stands for PCA, E for 1D-EWT, and C for 3D-CNN model. Prior to features extraction, the HI undergoes spectral dimension reduction via Principal Component Anaalysis (PCA). To forecast segmentation for a volumetric area of a 3D HI sample, 3D CNNs use 3D convolutional kernels. The usage of more parameters by these CNNs means that the ability to employ interslice context that can boost performance. The CNN model's parameters are optimised using a limited training set. The newly proposed PEC framework achieves a considerable overall accuracy of 99.58% and 99.94% (percentage increase of 0.08% and 0.54%) and Kappa score of 99.51% and 99.92 (percentage increase of 1.5% and 0.8%) compared to similar approaches for two real-world data sets IP and PU respectively with 30% training samples. [ABSTRACT FROM AUTHOR]