MSDANet: A Multiscale Dual-Channel Spatial Attention Network with Depthwise Separable Convolution for Hyperspectral Image Classification

Abstract

Hyperspectral image classification has garnered significant attention due to its crucial applications in terrain identification and scene understanding. However, the complexity of high-dimensional spectral data, high interclass spectral similarity, and diverse spatial scales present substantial challenges for classification tasks. This paper introduces a novel multiscale dual-channel spatial attention network (MSDANet) for hyperspectral image classification, which innovatively combines multiscale feature extraction with a dual-channel spatial attention to enhance classification performance. Specifically, MSDANet implements multiscale feature extraction through parallel multibranch structures and dilated convolutions, improving the model’s adaptability to targets at different scales. Additionally, we design a dual-channel spatial attention mechanism that integrates channel and spatial attention to achieve adaptive enhancement of spectral and spatial features. The incorporation of depthwise separable convolutions and lightweight attention modules significantly reduces computational complexity. Furthermore, an innovative feature fusion strategy employing residual connections and adaptive fusion enhances feature extraction effectiveness. Experiments conducted on three benchmark datasets demonstrate the superior classification performance of the proposed MSDANet approach. The method achieves overall classification accuracies of 96.07%, 96.85%, and 94.20% on the Indian Pines, Pavia University, and Kennedy Space Center datasets, respectively, significantly outperforming existing methods. Comprehensive ablation studies validate the effectiveness of each innovative component, with results indicating strong generalization capabilities in handling complex scenes and few-shot learning scenarios. These technical strengths make MSDANet particularly valuable for real-world applications, including precision agriculture for accurate crop type identification and health monitoring, and forestry management for precise species classification and sustainable resource assessment.