Acta Photonica Sinica, Volume. 52, Issue 12, 1210002(2023)

Spectral-spatial Attention Residual Networks for Hyperspectral Image Classification

Feifei WANG1,3, Huijie ZHAO1,2,3, Na LI1,2,3、*, Siyuan LI4, and Yu CAI5
Author Affiliations
  • 1Key Laboratory of Precision Opto-Mechatronics Technology,Ministry of Education,School of Instrumentation and Optoelectronic Engineering,Beihang University,Beijing 100191,China
  • 2Institute of Artificial Intelligence,Beihang University,Beijing 100191,China
  • 3Aerospace Optical-Microwave Integrated Precision Intelligent Sensing,Key Laboratory of Ministry of Industry and Information Technology,Beihang University,Beijing 100191,China
  • 4Key Laboratory of Spectral Imaging Technology,Xi'an Institute of Optics and Precision Mechanics,Chinese Academy of Sciences,Xi'an 710119,China
  • 5China Academy of Launch Vehicle Technology,Beijing 100076,China
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    References(25)

    [1] RASTI B, HONG Danfeng, HANG Renlong et al. Feature extraction for hyperspectral imagery: the evolution from shallow to deep(overview and toolbox)[J]. IEEE Geoscience and Remote Sensing Magazine, 8, 60-88(2020).

    [2] HINTON G E, SALAKHUTDINOV R R. Reducing the dimensionality of data with neural networks[J]. Science, 313, 504-507(2006).

    [3] HU Wei, HUANG Yangyu, WEI Li et al. Deep convolutional neural networks for hyperspectral image classification[J]. Journal of Sensors, 2015, 1-12(2015).

    [4] MOU Lichao, GHAMISI P, ZHU Xiaoxiang. Deep recurrent neural networks for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 55, 3639-3655(2017).

    [5] ZHONG Zilong, LI Ying, MA Lingfei et al. Spectral-spatial transformer network for hyperspectral image classification: a factorized architecture search framework[J]. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-15(2021).

    [6] GHADERIZADEH S, ABASIMOGHADAM D, SHARIFI A et al. Hyperspectral image classification using a hybrid 3D-2D convolutional neural networks[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 7570-7588(2021).

    [7] WU Hao, PRASAD S. Convolutional recurrent neural networks for hyperspectral data classifification[J]. Remote Sensing, 9, 298(2017).

    [8] ZHONG Zilong, LI J, LUO Zhiming et al. Spectral-spatial residual network for hyperspectral image classification: a 3-D deep learning framework[J]. IEEE Transactions on Geoscience and Remote Sensing, 56, 847-858(2017).

    [9] SHI Yuetian, FU Bin, WANG Nan et al. Spectral-spatial residual network for hyperspectral image classification: a 3-D deep learning framework[J]. Drones, 7, 1-30(2023).

    [10] XU Yue, GONG Jianya, HUANG Xin et al. Luojia-HSSR: a high spatial-spectral resolution remote sensing dataset for land-cover classification with a new 3D-HRNet[J]. Geo-spatial Information Science, 1-13(2022).

    [11] YANG Kai, SUN Hao, ZOU Chunbo et al. Cross-attention spectral-spatial network for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-14(2021).

    [12] ZHENG Xiangtao, SUN Hao, LU Xiaoqiang et al. Rotation-invariant attention network for hyperspectral image classification[J]. IEEE Transactions on Image Processing, 31, 4251-4265(2022).

    [13] FANG Shuai, ZHANG Kun, ZHANG Jing et al. Hyperspectral image classification with enhanced class separability[J]. Journal of Image and Graphics, 26, 1940-1951(2021).

    [14] VASWANI A, SHAZEER N, PARMAR N et al. Attention is all you need[C], 30, 1-11(2017).

    [15] LUO Fulin, ZHANG Liangpei, ZHOU Xiaocheng et al. Sparse-adaptive hypergraph discriminant analysis for hyperspectral image classification[J]. IEEE Geoscience and Remote Sensing Letters, 17, 1082-1086(2019).

    [16] VEITi A, WILBER M J, BELONGIE S. Residual networks behave like ensembles of relatively shallow networks[C], 550-558(2016).

    [17] HUANG Gao, LIU Zhuang, MAATEN L D M et al. Densely connected convolutional networks[C], 4700-4708(2017).

    [18] HE Kaiming, ZHANG Xiangyu, REN Shaoping et al. Deep residual learning for image recognition[C], 770-778(2016).

    [19] HE Kaiming, ZHANG Xiangyu, REN Shaoping et al. Identity mappings in deep residual networks[C], 630-645(2016).

    [20] PAOLETTI M E, HAUT J M, FERNANDEZ B R et al. Deep pyramidal residual networks for spectral-spatial hyperspectral image classifification[J]. IEEE Transactions on Geoscience and Remote Sensing, 57, 740-754(2019).

    [21] WANG Wenju, DOU Shuguang, Jiang Zhongmin et al. A fast dense spectral-spatial convolution network framework for hyperspectral images classifification[J]. Remote Sensing, 10, 1068(2018).

    [22] LUO Yanan, ZOU Jie, YAO Chengfei et al. HSI-CNN: a novel convolution neural network for hyperspectral image[C], 464-469(2018).

    [23] LI Ying, ZHANG Haokui, SHEN Qiang. Spectral-spatial classification of hyperspectral imagery with 3D convolutional neural network[J]. Remote Sensing, 9, 67(2017).

    [24] ROY S K, KRISHNA G, DUBEY S R et al. HybridSN: Exploring 3-D-2-D CNN feature hierarchy for hyperspectral image classification[J]. IEEE Geoscience and Remote Sensing Letters, 17, 277-281(2019).

    [25] SUN Le, ZHAO Guangrui, ZHENG Yuhui et al. Spectral-spatial feature tokenization transformer for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-14(2022).

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    Feifei WANG, Huijie ZHAO, Na LI, Siyuan LI, Yu CAI. Spectral-spatial Attention Residual Networks for Hyperspectral Image Classification[J]. Acta Photonica Sinica, 2023, 52(12): 1210002

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    Paper Information

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    Received: Apr. 18, 2023

    Accepted: May. 25, 2023

    Published Online: Feb. 19, 2024

    The Author Email: Na LI (lina_17@buaa.edu.cn)

    DOI:10.3788/gzxb20235212.1210002

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