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A Hybrid-Order Spectral-Spatial Feature Network for Hyperspectral Image Classification
D. X. Liu; G. L. Han; P. X. Liu; Y. R. Wang; H. Yang; D. B. Chen; Q. Q. Li and J. J. Wu
2022
发表期刊Remote Sensing
卷号14期号:15页码:28
摘要Convolutional neural networks are widely applied in hyperspectral image (HSI) classification and show excellent performance. However, there are two challenges: the first is that fine features are generally lost in the process of depth transfer; the second is that most existing studies usually restore to first-order features, whereas they rarely consider second-order representations. To tackle the above two problems, this article proposes a hybrid-order spectral-spatial feature network (HS(2)FNet) for hyperspectral image classification. This framework consists of a precedent feature extraction module (PFEM) and a feature rethinking module (FRM). The former is constructed to capture multiscale spectral-spatial features and focus on adaptively recalibrate channel-wise and spatial-wise feature responses to achieve first-order spectral-spatial feature distillation. The latter is devised to heighten the representative ability of HSI by capturing the importance of feature cross-dimension, while learning more discriminative representations by exploiting the second-order statistics of HSI, thereby improving the classification performance. Massive experiments demonstrate that the proposed network achieves plausible results compared with the state-of-the-art classification methods.
DOI10.3390/rs14153555
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收录类别sci ; ei
语种英语
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文献类型期刊论文
条目标识符http://ir.ciomp.ac.cn/handle/181722/66739
专题中国科学院长春光学精密机械与物理研究所
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D. X. Liu,G. L. Han,P. X. Liu,et al. A Hybrid-Order Spectral-Spatial Feature Network for Hyperspectral Image Classification[J]. Remote Sensing,2022,14(15):28.
APA D. X. Liu.,G. L. Han.,P. X. Liu.,Y. R. Wang.,H. Yang.,...&Q. Q. Li and J. J. Wu.(2022).A Hybrid-Order Spectral-Spatial Feature Network for Hyperspectral Image Classification.Remote Sensing,14(15),28.
MLA D. X. Liu,et al."A Hybrid-Order Spectral-Spatial Feature Network for Hyperspectral Image Classification".Remote Sensing 14.15(2022):28.
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