Enhancing land cover object classification in hyperspectral imagery through an efficient spectral-spatial feature learning approach.

The classification of land cover objects in hyperspectral imagery (HSI) has significantly advanced due to the development of convolutional neural networks (CNNs). However, challenges such as limited training data and high dimensionality negatively impact classification performance. Traditional CNN-based methods predominantly utilize 2D CNNs for feature extraction, which inadequately exploit the inter-band correlations in HSIs. While 3D CNNs can capture joint spectral-spatial information, they often encounter issues related to network depth and complexity. To address these issues, we propose an innovative land cover object classification approach in HSIs that integrates segmented principal component analysis (Seg-PCA) with hybrid 3D-2D CNNs. Our approach leverages Seg-PCA for effective feature extraction and employs the minimum-redundancy maximum relevance (mRMR) criterion for feature selection. By combining the strengths of both 3D and 2D CNNs, our method efficiently extracts spectral-spatial features. These features are then processed through fully connected dense layers and a softmax layer for classification. Extensive experiments on three widely used HSI datasets demonstrate that our method consistently outperforms existing state-of-the-art techniques in classification performance. These results highlight the efficacy of our approach and its potential to significantly enhance the classification of land cover objects in hyperspectral imagery.