Segmentation-based deep 2D-3D multibranch learning approach for effective hyperspectral image classification.

Deep learning has revolutionized the classification of land cover objects in hyperspectral images (HSIs), particularly by managing the complex 3D cube structure inherent in HSI data. Despite these advances, challenges such as data redundancy, computational costs, insufficient sample sizes, and the curse of dimensionality persist. Traditional 2D Convolutional Neural Networks (CNNs) struggle to fully leverage the interconnections between spectral bands in HSIs, while 3D CNNs, which capture both spatial and spectral features, require more sophisticated design. To address these issues, we propose a novel multilayered, multi-branched 2D-3D CNN model in this paper that integrates Segmented Principal Component Analysis (SPCA) and the minimum-Redundancy-Maximum-Relevance (mRMR) technique. This approach explores the local structure of the data and ranks features by significance. Our approach then hierarchically processes these features: the shallow branch handles the least significant features, the deep branch processes the most critical features, and the mid branch deals with the remaining features. Experimental results demonstrate that our proposed method outperforms most of the state-of-the-art techniques on the Salinas Scene, University of Pavia, and Indian Pines hyperspectral image datasets achieving 100%, 99.94%, and 99.12% Overall Accuracy respectively.