A hybrid approach consisting of 3D depthwise separable convolution and depthwise squeeze-and-excitation network for hyperspectral image classification

Abstract

Hyperspectral image classification is crucial for a wide range of applications, including environmental monitoring, precision agriculture, and mining, due to its ability to capture detailed spectral information across numerous wavelengths. However, the high dimensionality and complex spatial-spectral relationships in hyperspectral data pose significant challenges. Deep learning, particularly Convolutional Neural Networks (CNNs), has shown remarkable success in automatically extracting relevant features from high-dimensional data, making them well-suited for handling the intricate spatial-spectral relationships in hyperspectral images.This study presents a hybrid approach for hyperspectral image classification, combining 3D Depthwise Separable Convolution (3D DSC) and Depthwise Squeeze-and-Excitation Network (DSENet). The 3D DSC efficiently captures spatial-spectral features, reducing computational complexity while preserving essential information. The DSENet further refines these features by applying channel-wise attention, enhancing the model's ability to focus on the most informative features. To assess the performance of the proposed hybrid model, extensive experimental studies were carried out on four commonly utilized HSI datasets, namely HyRANK-Loukia and WHU-Hi (including HongHu, HanChuan, and LongKou). As a result of the experimental studies, the HyRANK-Loukia achieved an accuracy of 90.9%, marking an 8.86% increase compared to its previous highest accuracy. Similarly, for the WHU-Hi datasets, HongHu achieved an accuracy of 97.49%, reflecting a 2.11% improvement over its previous highest accuracy; HanChuan achieved an accuracy of 97.49%, showing a 2.4% improvement; and LongKou achieved an accuracy of 99.79%, providing a 0.15% improvement compared to its previous highest accuracy. Comparative analysis highlights the superiority of the proposed model, emphasizing improved classification accuracy with lower computational costs.