Spectral-spatial multi-layer perceptron network for hyperspectral image land cover classification

This paper proposes a novel spectral-spatial multi-layer perceptron network for hyperspectral image land cover classification. Current deep learning-based methods have limitations in spectral and spatial feature representation of hyperspectral images, and these shortcomings will severely restrict the hyperspectral image classification performance. The proposed spectral-spatial multi-layer perceptron network exclusively utilizes multi-layer perceptron to represent and classify hyperspectral images. Specifically, the spectral multi-layer perceptron is investigated to model the long-range dependencies along the spectral dimension, because all diagnostic spectral bands contribute to classification performance. Then, we exploit the spatial multi-layer perceptron to extract local spatial features from hyperspectral data, which are also crucial for land cover classification. Furthermore, global spectral characteristics and local spatial features are integrated to perform the hyperspectral image spectral-spatial classification. Three benchmark hyperspectral datasets are employed for comparative classification experiments and ablation study, and experimental results certify the effectiveness and advancement of the proposed model in terms of collaborative classification accuracy.

Automated summary

This paper proposes a novel spectral-spatial multi-layer perceptron network for hyperspectral image land cover classification. The network utilizes multi-layer perceptron to represent and classify hyperspectral images, which improves the classification performance compared to current deep learning methods. Experimental results certify the effectiveness and advancement of the proposed model in terms of collaborative classification accuracy.