In hyperspectral remote sensing imagery, pixel interactions within defined spatial extents result in the mixing of adjacent pixels. Additionally, the high similarity of adjacent spectra leads to information redundancy,which hinders the extraction of global spatial and spectral correlations. In order to solve the problems of mixed adjacent pixels and redundant adjacent spectra, this work offers a hyperspectral image classification approach that uses a global space-spectral attention mechanism. First, the proposed method's global spatial attention module uses multi-scale dilated convolution to produce a bigger receptive field to be capable of capturing global spatial correlation and obtain unmixed pixel information. Then, the global spectral attention module designs a spectral domain partition algorithm, using the combination of regional density as well as information entropy as the threshold to divide spectrum into dispersed subsets and eliminate redundant information. The global context information for entire spectral band is fully exploited, and correlation of the global spectral information is extracted. Finally, the two modules combine to provide a global correlation of space and spectrum. Experiments demonstrate that the suggested method obtains overall accuracies of 97.28%, 94.73%, and 95.76% on the three WHU-Hi hyperspectral datasets, surpassing comparison methods.
Qin Rui, Wu Benze, Liu Xinfu, Wu Yirui
. Hyperspectral Image Classification Method Based on Global Space-Spectral Attention Mechanism[J]. Journal of Shanghai Jiaotong University(Science), 2026
, 31(4)
: 909
-919
.
DOI: 10.1007/s12204-024-2792-2
[1] ZHONG Z L, LI J, LUO Z M, et al. Spectral–spatial residual network for hyperspectral image classification: A 3-D deep learning framework [J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(2): 847-858.
[2] WANG T, HU S M, SUN J G. Image retrieval based on color-spatial feature [J]. Journal of Software, 2002, 13(10): 2031-2036 (in Chinese).
[3] LI X M, ZHENG L, HU Z Y. SIFT based automatic registration of remotely-sensed imagery [J]. Journal of Remote Sensing, 2006, 10(6): 885-892 (in Chinese).
[4] QI S X, MA J, LIN J, et al. Unsupervised ship detection based on saliency and S-HOG descriptor from optical satellite images [J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(7): 1451-1455.
[5] MELGANI F, BRUZZONE L. Classification of hyperspectral remote sensing images with support vector machines [J]. IEEE Transactions on Geoscience and Remote Sensing, 2004, 42(8): 1778-1790.
[6] ZHOU Y M, WU Y R, SHEN X J, et al. Multi-kernel ensemble SVM for SAR target classification [J]. Command Information System and Technology, 2022, 13(3): 36-43 (in Chinese).
[7] KRISHNA L S, DATT K M, RAMESH Y R, et al. A novel framework for classfication of hyperspectral images [C]//2022 7th International Conference on Communication and Electronics Systems. Coimbatore: IEEE, 2022: 1704-1709.
[8] YANG W Q, LIU J, CHEN Z, et al. Recognition method of ship identification number based on deep learning [J]. Command Information System and Technology, 2022, 13(1): 58-63 (in Chinese).
[9] SUN H, ZHENG X T, LU X Q, et al. Spectral–spatial attention network for hyperspectral image classification [J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(5): 3232-3245.
[10] ROY S K, MANNA S, SONG T C, et al. Attention-based adaptive spectral–spatial kernel ResNet for hyperspectral image classification [J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(9): 7831-7843.
[11] GAO Z L, XIE J T, WANG Q L, et al. Global second-order pooling convolutional networks [C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach: IEEE, 2019: 3019-3028.
[12] YANG Z X, ZHU L C, WU Y, et al. Gated channel transformation for visual recognition [C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle: IEEE, 2020: 11791-11800.
[13] QIN Z Q, ZHANG P Y, WU F, et al. FcaNet: Frequency channel attention networks [C]//2021 IEEE/CVF International Conference on Computer Vision. Montreal: IEEE, 2021: 763-772.
[14] JADERBERG M, SIMONYAN K, ZISSERMAN A, et al. Spatial transformer networks [C]// 29th Conference on Neural Information Processing Systems. Montreal: NIPS, 2015: 1-9.
[15] MNIH V, HEESS N, GRAVES A. Recurrent models of visual attention [C]// 28th Conference on Neural Information Processing Systems. Montreal: NIPS, 2014: 1-9.
[16] ZHONG Y F, HU X, LUO C, et al. WHU-Hi: UAV-borne hyperspectral with high spatial resolution (H2) benchmark datasets and classifier for precise crop identification based on deep convolutional neural network with CRF [J]. Remote Sensing of Environment, 2020, 250: 112012.
[17] CHEN Y S, LIN Z H, ZHAO X, et al. Deep learning-based classification of hyperspectral data [J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(6): 2094-2107.
[18] PAOLETTI M E, HAUT J M, FERNANDEZ-BELTRAN R, et al. Deep pyramidal residual networks for spectral–spatial hyperspectral image classification [J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(2): 740-754.
