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• W4282595292 abstract "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." @default.
• W4282595292 created "2022-06-14" @default.
• W4282595292 creator A5001486765 @default.
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• W4282595292 date "2022-06-13" @default.
• W4282595292 modified "2023-10-18" @default.
• W4282595292 title "Spectral-spatial multi-layer perceptron network for hyperspectral image land cover classification" @default.
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• W4282595292 doi "https://doi.org/10.1080/22797254.2022.2087540" @default.
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