Central Attention Network for Hyperspectral Imagery Classification

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IEEE Transactions on Neural Networks and Learning Systems
In this article, the intrinsic properties of hyperspectral imagery (HSI) are analyzed, and two principles for spectral-spatial feature extraction of HSI are built, including the foundation of pixel-level HSI classification and the definition of spatial information. Based on the two principles, scaled dot-product central attention (SDPCA) tailored for HSI is designed to extract spectral-spatial information from a central pixel (i.e., a query pixel to be classified) and pixels that are similar to the central pixel on an HSI patch. Then, employed with the HSI-tailored SDPCA module, a central attention network (CAN) is proposed by combining HSI-tailored dense connections of the features of the hidden layers and the spectral information of the query pixel. MiniCAN as a simplified version of CAN is also investigated. Superior classification performance of CAN and miniCAN on three datasets of different scenarios demonstrates their effectiveness and benefits compared with state-of-the-art methods.
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Central attention, hyperspectral imagery (HSI), spectral-spatial feature extraction, transformer
H. Liu, W. Li, X. -G. Xia, M. Zhang, C. -Z. Gao and R. Tao, "Central Attention Network for Hyperspectral Imagery Classification," in IEEE Transactions on Neural Networks and Learning Systems, doi: 10.1109/TNNLS.2022.3155114.