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• W3214894844 endingPage "4823" @default.
• W3214894844 startingPage "4823" @default.
• W3214894844 abstract "Multi-sensor image can provide supplementary information, usually leading to better performance in classification tasks. However, the general deep neural network-based multi-sensor classification method learns each sensor image separately, followed by a stacked concentrate for feature fusion. This way requires a large time cost for network training, and insufficient feature fusion may cause. Considering efficient multi-sensor feature extraction and fusion with a lightweight network, this paper proposes an attention-guided classification method (AGCNet), especially for multispectral (MS) and panchromatic (PAN) image classification. In the proposed method, a share-split network (SSNet) including a shared branch and multiple split branches performs feature extraction for each sensor image, where the shared branch learns basis features of MS and PAN images with fewer learn-able parameters, and the split branch extracts the privileged features of each sensor image via multiple task-specific attention units. Furthermore, a selective classification network (SCNet) with a selective kernel unit is used for adaptive feature fusion. The proposed AGCNet can be trained by an end-to-end fashion without manual intervention. The experimental results are reported on four MS and PAN datasets, and compared with state-of-the-art methods. The classification maps and accuracies show the superiority of the proposed AGCNet model." @default.
• W3214894844 created "2021-12-06" @default.
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• W3214894844 date "2021-11-27" @default.
• W3214894844 modified "2023-10-06" @default.
• W3214894844 title "Attention-Guided Multispectral and Panchromatic Image Classification" @default.
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• W3214894844 doi "https://doi.org/10.3390/rs13234823" @default.
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