{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:58:10Z","timestamp":1760144290433,"version":"build-2065373602"},"reference-count":47,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key Laboratory of Intelligent Health Perception and Ecological Restoration of Rivers and Lakes, Ministry of Education, Hubei University of Technology","award":["HGKFZP014","41901296","21067"],"award-info":[{"award-number":["HGKFZP014","41901296","21067"]}]},{"name":"National Natural Science Foundation of China","award":["HGKFZP014","41901296","21067"],"award-info":[{"award-number":["HGKFZP014","41901296","21067"]}]},{"name":"Hubei University of Technology Research and Innovation Program","award":["HGKFZP014","41901296","21067"],"award-info":[{"award-number":["HGKFZP014","41901296","21067"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Hyperspectral image (HSI) classification aims to recognize categories of objects based on spectral\u2013spatial features and has been used in a wide range of real-world application areas. Attention mechanisms are widely used in HSI classification for their ability to focus on important information in images automatically. However, due to the approximate spectral\u2013spatial features in HSI, mainstream attention mechanisms are difficult to accurately distinguish the small difference, which limits the classification accuracy. To overcome this problem, a spectral\u2013spatial-sensorial attention network (S3AN) with controllable factors is proposed to efficiently recognize different objects. Specifically, two controllable factors, dynamic exponential pooling (DE-Pooling) and adaptive convolution (Adapt-Conv), are designed to enlarge the difference in approximate features and enhance the attention weight interaction. Then, attention mechanisms with controllable factors are utilized to build the redundancy reduction module (RRM), feature learning module (FLM), and label prediction module (LPM) to process HSI spectral\u2013spatial features. The RRM utilizes the spectral attention mechanism to select representative band combinations, and the FLM introduces the spatial attention mechanism to highlight important objects. Furthermore, the sensorial attention mechanism extracts location and category information in a pseudo label to guide the LPM for label prediction and avoid details from being ignored. Experimental results on three public HSI datasets show that the proposed method is able to accurately recognize different objects with an overall accuracy (OA) of 98.69%, 98.89%, and 97.56%, respectively.<\/jats:p>","DOI":"10.3390\/rs16071253","type":"journal-article","created":{"date-parts":[[2024,4,2]],"date-time":"2024-04-02T01:26:10Z","timestamp":1712021170000},"page":"1253","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Spectral-Spatial-Sensorial Attention Network with Controllable Factors for Hyperspectral Image Classification"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3568-1654","authenticated-orcid":false,"given":"Sheng","family":"Li","sequence":"first","affiliation":[{"name":"School of Computer Science, Hubei University of Technology, Wuhan 430068, China"}]},{"given":"Mingwei","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Hubei University of Technology, Wuhan 430068, China"}]},{"given":"Chong","family":"Cheng","sequence":"additional","affiliation":[{"name":"School of Computer Science, Hubei University of Technology, Wuhan 430068, China"}]},{"given":"Xianjun","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Geosciences, Yangtze University, Wuhan 430100, China"}]},{"given":"Zhiwei","family":"Ye","sequence":"additional","affiliation":[{"name":"School of Computer Science, Hubei University of Technology, Wuhan 430068, China"}]},{"given":"Wei","family":"Liu","sequence":"additional","affiliation":[{"name":"Institute of Geological Survey, China University of Geosciences, Wuhan 430074, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1579","DOI":"10.1109\/TGRS.2017.2765364","article-title":"Recent advances on spectral\u2013spatial hyperspectral image classification: An overview and new guidelines","volume":"56","author":"He","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1016\/j.isprsjprs.2019.09.006","article-title":"Deep learning classifiers for hyperspectral imaging: A review","volume":"158","author":"Paoletti","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3185","DOI":"10.1109\/TCYB.2020.3004263","article-title":"Spectral\u2013spatial weighted kernel manifold embedded distribution alignment for remote sensing image classification","volume":"51","author":"Dong","year":"2021","journal-title":"IEEE Trans. Cybern."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1082","DOI":"10.1109\/TGRS.2014.2333539","article-title":"Dimension reduction using spatial and spectral regularized local discriminant embedding for hyperspectral image classification","volume":"53","author":"Zhou","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1667","DOI":"10.1109\/TCYB.2015.2453359","article-title":"Learning hierarchical spectral\u2013spatial features for hyperspectral image classification","volume":"46","author":"Zhou","year":"2016","journal-title":"IEEE Trans. Cybern."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.neucom.2015.07.132","article-title":"Hierarchical feature learning with dropout k-means for hyperspectral image classification","volume":"187","author":"Zhang","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.knosys.2018.12.031","article-title":"A feature selection approach for hyperspectral image based on modified ant lion optimizer","volume":"168","author":"Wang","year":"2019","journal-title":"Knowl. Based Syst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1109\/TGRS.2017.2744662","article-title":"Random forest ensembles and extended multiextinction profiles for hyperspectral image classification","volume":"56","author":"Xia","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"7766","DOI":"10.1007\/s10489-021-02270-0","article-title":"A band selection approach based on wavelet support vector machine ensemble model and membrane whale optimization algorithm for hyperspectral image","volume":"51","author":"Wang","year":"2021","journal-title":"Appl. Intell."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"7738","DOI":"10.1109\/TGRS.2014.2318058","article-title":"Spectral\u2013spatial hyperspectral image classification via multiscale adaptive sparse representation","volume":"52","author":"Fang","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"106234","DOI":"10.1016\/j.engappai.2023.106234","article-title":"Quaternion convolutional neural networks for hyperspectral image classification","volume":"123","author":"Zhou","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_12","first-page":"6009005","article-title":"Convolutional transformer network for hyperspectral image classification","volume":"19","author":"Zhao","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1109\/LGRS.2019.2918719","article-title":"HybridSN: Exploring 3-D\u20132-D CNN feature hierarchy for hyperspectral image classification","volume":"17","author":"Roy","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"109650","DOI":"10.1016\/j.asoc.2022.109650","article-title":"Compression and reinforce variation with convolutional neural networks for hyperspectral image classification","volume":"130","author":"Dalal","year":"2022","journal-title":"Appl. Soft Comput."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"847","DOI":"10.1109\/TGRS.2017.2755542","article-title":"Spectral\u2013spatial residual network for hyperspectral image classification: A 3-D Deep Learning Framework","volume":"56","author":"Zhong","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"492","DOI":"10.1109\/TBDATA.2019.2923243","article-title":"Beyond the patchwise classification: Spectral-spatial fully convolutional networks for hyperspectral image classification","volume":"6","author":"Xu","year":"2020","journal-title":"IEEE Trans. Big Data"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"5612","DOI":"10.1109\/TGRS.2020.2967821","article-title":"FPGA: Fast patch-free global learning framework for fully end-to-end hyperspectral image classification","volume":"58","author":"Zheng","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","first-page":"5528715","article-title":"Hyperspectral image transformer classification networks","volume":"60","author":"Yang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"6896","DOI":"10.1109\/TPAMI.2020.3007032","article-title":"Criss-cross attention for semantic segmentation","volume":"45","author":"Huang","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_20","first-page":"5501505","article-title":"MS2CANet: Multiscale spatial\u2013spectral cross-modal attention network for hyperspectral image and LiDAR classification","volume":"21","author":"Wang","year":"2024","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2011","DOI":"10.1109\/TPAMI.2019.2913372","article-title":"Squeeze-and-excitation networks","volume":"42","author":"Hu","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., and Kweon, I.S. (2018, January 8\u201314). Cbam: Convolutional block attention module. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., and Hu, Q. (2020, January 13\u201319). ECA-Net: Efficient channel attention for deep convolutional neural networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"5527219","DOI":"10.1109\/TGRS.2023.3321840","article-title":"A feature complementary attention network based on adaptive knowledge filtering for hyperspectral image classification","volume":"61","author":"Shi","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"109123","DOI":"10.1016\/j.patcog.2022.109123","article-title":"Binary feature learning with local spectral context-aware attention for classification of hyperspectral images","volume":"134","author":"Xing","year":"2023","journal-title":"Pattern Recognit."},{"key":"ref_26","first-page":"5503905","article-title":"Spectral-spatial graph attention network for semisupervised hyperspectral image classification","volume":"19","author":"Zhao","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Hou, Q., Zhou, D., and Feng, J. (2021, January 20\u201325). Coordinate attention for efficient mobile network design. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Wang, F., Jiang, M., Qian, C., Yang, S., Li, C., Zhang, H., Wang, X., and Tang, X. (2017, January 21\u201326). Residual attention network for image classification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.683"},{"key":"ref_29","first-page":"5502418","article-title":"Cross-domain few-shot hyperspectral image classification with class-wise attention","volume":"61","author":"Wang","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"5503615","DOI":"10.1109\/TGRS.2023.3242346","article-title":"Spectral\u2013spatial morphological attention transformer for hyperspectral image classification","volume":"61","author":"Roy","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","first-page":"5501005","article-title":"Convolution transformer fusion splicing network for hyperspectral image classification","volume":"20","author":"Zhao","year":"2023","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1109\/TGRS.2017.2748160","article-title":"Unsupervised spectral\u2013spatial feature learning via deep residual Conv\u2013Deconv network for hyperspectral image classification","volume":"56","author":"Mou","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2500518","DOI":"10.1109\/TIM.2023.3323997","article-title":"Dynamic low-rank and sparse priors constrained deep autoencoders for hyperspectral anomaly detection","volume":"73","author":"Lin","year":"2024","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1968","DOI":"10.1109\/LGRS.2019.2960528","article-title":"DSSNet: A simple dilated semantic segmentation network for hyperspectral imagery classification","volume":"17","author":"Pan","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"115663","DOI":"10.1016\/j.eswa.2021.115663","article-title":"An attention-driven convolutional neural network-based multi-level spectral\u2013spatial feature learning for hyperspectral image classification","volume":"185","author":"Pu","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Li, R., Zheng, S., Duan, C., Yang, Y., and Wang, X. (2020). Classification of hyperspectral image based on double-branch dual-attention mechanism network. Remote Sens., 12.","DOI":"10.20944\/preprints201912.0059.v2"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1109\/TGRS.2020.2994057","article-title":"Residual spectral\u2013spatial attention network for hyperspectral image classification","volume":"59","author":"Zhu","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"3232","DOI":"10.1109\/TGRS.2019.2951160","article-title":"Spectral\u2013spatial attention network for hyperspectral image classification","volume":"58","author":"Sun","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","first-page":"5002016","article-title":"Deep self-representation learning framework for hyperspectral anomaly detection","volume":"73","author":"Cheng","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1969","DOI":"10.1109\/TGRS.2019.2951433","article-title":"BS-Nets: An end-to-end framework for band selection of hyperspectral image","volume":"58","author":"Cai","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"118797","DOI":"10.1016\/j.eswa.2022.118797","article-title":"TAttMSRecNet: Triplet-attention and multiscale reconstruction network for band selection in hyperspectral images","volume":"212","author":"Nandi","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"6232","DOI":"10.1109\/TGRS.2016.2584107","article-title":"Deep feature extraction and classification of hyperspectral images based on convolutional neural networks","volume":"54","author":"Chen","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"9540","DOI":"10.1109\/TGRS.2021.3053397","article-title":"Compact band weighting module based on attention-driven for hyperspectral image classification","volume":"59","author":"Zhao","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"4843","DOI":"10.1109\/TIP.2017.2725580","article-title":"Going deeper with contextual CNN for hyperspectral image classification","volume":"26","author":"Lee","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"108995","DOI":"10.1016\/j.sigpro.2023.108995","article-title":"Exponential linear units-guided Depthwise separable convolution network with cross attention mechanism for hyperspectral image classification","volume":"210","author":"Gao","year":"2023","journal-title":"Signal Process."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"5507905","DOI":"10.1109\/LGRS.2023.3303008","article-title":"Hyperspectral image classification based on interactive transformer and CNN with multilevel feature fusion network","volume":"20","author":"Yang","year":"2023","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1016\/j.patrec.2021.01.015","article-title":"Adaptive hybrid attention network for hyperspectral image classification","volume":"144","author":"Shivam","year":"2021","journal-title":"Pattern Recognit. Lett."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/7\/1253\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:22:17Z","timestamp":1760106137000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/7\/1253"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,1]]},"references-count":47,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2024,4]]}},"alternative-id":["rs16071253"],"URL":"https:\/\/doi.org\/10.3390\/rs16071253","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2024,4,1]]}}}