{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T15:01:06Z","timestamp":1772895666390,"version":"3.50.1"},"reference-count":56,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2021,9,9]],"date-time":"2021-09-09T00:00:00Z","timestamp":1631145600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41701479, 62071084"],"award-info":[{"award-number":["41701479, 62071084"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Project plan of Science Foundation of Heilongjiang Province of China","award":["QC2018045"],"award-info":[{"award-number":["QC2018045"]}]},{"name":"Fundamental Research Funds in Heilongjiang Provincial Universities of China","award":["135509136"],"award-info":[{"award-number":["135509136"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Convolutional neural networks (CNNs) have exhibited excellent performance in hyperspectral image classification. However, due to the lack of labeled hyperspectral data, it is difficult to achieve high classification accuracy of hyperspectral images with fewer training samples. In addition, although some deep learning techniques have been used in hyperspectral image classification, due to the abundant information of hyperspectral images, the problem of insufficient spatial spectral feature extraction still exists. To address the aforementioned issues, a spectral\u2013spatial attention fusion with a deformable convolution residual network (SSAF-DCR) is proposed for hyperspectral image classification. The proposed network is composed of three parts, and each part is connected sequentially to extract features. In the first part, a dense spectral block is utilized to reuse spectral features as much as possible, and a spectral attention block that can refine and optimize the spectral features follows. In the second part, spatial features are extracted and selected by a dense spatial block and attention block, respectively. Then, the results of the first two parts are fused and sent to the third part, and deep spatial features are extracted by the DCR block. The above three parts realize the effective extraction of spectral\u2013spatial features, and the experimental results for four commonly used hyperspectral datasets demonstrate that the proposed SSAF-DCR method is superior to some state-of-the-art methods with very few training samples.<\/jats:p>","DOI":"10.3390\/rs13183590","type":"journal-article","created":{"date-parts":[[2021,9,9]],"date-time":"2021-09-09T21:36:58Z","timestamp":1631223418000},"page":"3590","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["A Spectral Spatial Attention Fusion with Deformable Convolutional Residual Network for Hyperspectral Image Classification"],"prefix":"10.3390","volume":"13","author":[{"given":"Tianyu","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Communication and Electronic Engineering, Qiqihar University, Qiqihar 161000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cuiping","family":"Shi","sequence":"additional","affiliation":[{"name":"College of Communication and Electronic Engineering, Qiqihar University, Qiqihar 161000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Diling","family":"Liao","sequence":"additional","affiliation":[{"name":"College of Communication and Electronic Engineering, Qiqihar University, Qiqihar 161000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liguo","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Information and Communication Engineering, Dalian Nationalities University, Dalian 116000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Chang, C.I. (2007). Hyperspectral Data Exploitation: Theory and Applications, John Wiley & Sons.","DOI":"10.1002\/0470124628"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2401","DOI":"10.1080\/01431160117383","article-title":"Study of crop growth parameters using airborne imaging spectrometer data","volume":"22","author":"Patel","year":"2001","journal-title":"Int. J. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1147","DOI":"10.1126\/science.228.4704.1147","article-title":"Imaging Spectrometry for Earth Remote Sensing","volume":"228","author":"Goetz","year":"1985","journal-title":"Science"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1080\/02693799308901949","article-title":"Artificial neural networks for land-cover classification and mapping","volume":"7","author":"Civco","year":"1993","journal-title":"Int. J. Geogr. Inf. Syst."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2565","DOI":"10.1109\/TGRS.2013.2263282","article-title":"Spectral\u2013Spatial Classification of Hyperspectral Images Based on Hidden Markov Random Fields","volume":"52","author":"Ghamisi","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1766","DOI":"10.1109\/TCSVT.2008.2004919","article-title":"A Robust Error Detection Mechanism for H.264\/AVC Coded Video Sequences Based on Support Vector Machines","volume":"18","author":"Farrugia","year":"2008","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1319","DOI":"10.1109\/TNNLS.2013.2293061","article-title":"Jointly Learning the Hybrid CRF and MLR Model for Simultaneous Denoising and Classification of Hyperspectral Imagery","volume":"25","author":"Zhong","year":"2014","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"4186","DOI":"10.1109\/TGRS.2015.2392755","article-title":"Spectral-spatial classification of hyper- spectral images with a superpixel-based discriminative sparse model","volume":"53","author":"Fang","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Fu, W., Li, S., and Fang, L. (2015, January 26\u201331). Spectral-spatial hyperspectral image classification via superpixel merging and sparse representation. Proceedings of the 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy.","DOI":"10.1109\/IGARSS.2015.7326948"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"6663","DOI":"10.1109\/TGRS.2015.2445767","article-title":"Classification of Hyperspectral Images by Exploiting Spectral\u2013Spatial Information of Superpixel via Multiple Kernels","volume":"53","author":"Fang","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1007\/s12145-017-0298-2","article-title":"An adaptive framework for spectral-spatial classification based on a combination of pixel-based and object-based scenarios","volume":"10","author":"Zehtabian","year":"2017","journal-title":"Earth Sci. Inform."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"905","DOI":"10.14358\/PERS.73.8.905","article-title":"The Importance of Scale in Object-based Mapping of Vegetation Parameters with Hyperspectral Imagery","volume":"73","author":"Addink","year":"2007","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zeng, D., Liu, K., Chen, Y., and Zhao, J. (2015, January 17\u201321). Distant Supervision for Relation Extraction via Piecewise Convolutional Neural Networks. Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, Lisbon, Portugal.","DOI":"10.18653\/v1\/D15-1203"},{"key":"ref_14","unstructured":"Gehring, J., Auli, M., Grangier, D., Yarats, D., and Dauphin, Y.N. (2017). Convolutional Sequence to Sequence Learning. arXiv."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"He, H., Gimpel, K., and Lin, J. (2015, January 26\u201331). Multi-Perspective Sentence Similarity Modeling with Convolutional Neural Networks. Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, Milan, Italy.","DOI":"10.18653\/v1\/D15-1181"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"8506","DOI":"10.1109\/TGRS.2019.2921342","article-title":"Adaptive Multiscale Deep Fusion Residual Network for Remote Sensing Image Classification","volume":"57","author":"Li","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_18","unstructured":"Wang, R.J., Li, X., and Ling, C.X. (2018). Pelee: A Real-Time Object Detection System on Mobile Devices. arXiv."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Sainath, T.N., Mohamed, A.-R., Kingsbury, B., and Ramabhadran, B. (2013, January 26\u201331). Deep convolutional neural networks for LVCSR. Proceedings of the 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, Vancouver, BC, Canada.","DOI":"10.1109\/ICASSP.2013.6639347"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"258619","DOI":"10.1155\/2015\/258619","article-title":"Deep Convolutional Neural Networks for Hyperspectral Image Classification","volume":"2015","author":"Hu","year":"2015","journal-title":"J. Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"844","DOI":"10.1109\/TGRS.2016.2616355","article-title":"Hyperspectral Image Classification Using Deep Pixel-Pair Features","volume":"55","author":"Li","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1412","DOI":"10.1109\/LGRS.2019.2899823","article-title":"Deep Hashing Neural Networks for Hyperspectral Image Feature Extraction","volume":"16","author":"Fang","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"755","DOI":"10.1109\/TGRS.2018.2860464","article-title":"Feature Extraction With Multiscale Covariance Maps for Hyperspectral Image Classification","volume":"57","author":"He","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1253","DOI":"10.1109\/LGRS.2017.2704625","article-title":"Deep Fusion of Remote Sensing Data for Accurate Classification","volume":"14","author":"Chen","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2623","DOI":"10.1109\/TIP.2018.2809606","article-title":"Diverse Region-Based CNN for Hyperspectral Image Classification","volume":"27","author":"Zhang","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1254","DOI":"10.1109\/LGRS.2018.2830403","article-title":"Deformable Convolutional Neural Networks for Hyperspectral Image Classification","volume":"15","author":"Zhu","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2354","DOI":"10.1109\/TIP.2018.2799324","article-title":"Hyperspectral Image Classification With Markov Random Fields and a Convolutional Neural Network","volume":"27","author":"Cao","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3173","DOI":"10.1109\/TGRS.2018.2794326","article-title":"Hyperspectral Image Classification With Deep Feature Fusion Network","volume":"56","author":"Song","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1909","DOI":"10.1109\/TGRS.2017.2769673","article-title":"Supervised Deep Feature Extraction for Hyperspectral Image Classification","volume":"56","author":"Liu","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3396","DOI":"10.1109\/TGRS.2020.3008286","article-title":"Multiscale Residual Network With Mixed Depthwise Convolution for Hyperspectral Image Classification","volume":"59","author":"Gao","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"3162","DOI":"10.1109\/TGRS.2019.2949180","article-title":"Multiscale Dynamic Graph Convolutional Network for Hyperspectral Image Classification","volume":"58","author":"Wan","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","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_33","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_34","first-page":"016005","article-title":"Spectral\u2013spatial classification of hyperspectral image using three-dimensional convolution network","volume":"12","author":"Liu","year":"2018","journal-title":"J. Appl. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Li, Y., Zhang, H., and Shen, Q. (2017). Spectral\u2013Spatial Classification of Hyperspectral Imagery with 3D Convolutional Neural Network. Remote Sens., 9.","DOI":"10.3390\/rs9010067"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1299","DOI":"10.1109\/JSTARS.2019.2900705","article-title":"CNN-Based Multilayer Spatial\u2013Spectral Feature Fusion and Sample Augmentation With Local and Nonlocal Constraints for Hyperspectral Image Classification","volume":"12","author":"Feng","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"4729","DOI":"10.1109\/TGRS.2017.2698503","article-title":"Learning and Transferring Deep Joint Spectral\u2013Spatial Features for Hyperspectral Classification","volume":"55","author":"Yang","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","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":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","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":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., van der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely Connected Convolutional Networks. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Fu, J., Liu, J., Tian, H., Li, Y., Bao, Y., Fang, Z., and Lu, H. (2019, January 16\u201320). Dual Attention Network for Scene Segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00326"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_43","unstructured":"Ioffe, S., and Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015, January 7\u201313). Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. Proceedings of the IEEE Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref_45","first-page":"1929","article-title":"Dropout: A Simple Way to Prevent Neural Networks from Overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_46","unstructured":"Nair, V., and Hinton, G.E. (2010, January 21\u201324). Rectified linear units improve restricted boltzmann machines. Proceedings of the 27th International Conference on Machine Learning, Haifa, Israel."},{"key":"ref_47","unstructured":"Misra, D. (2019). Mish: A Self Regularized Non-Monotonic Neural Activation Function. arXiv."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1804","DOI":"10.1109\/TGRS.2008.916090","article-title":"Nearest Neighbor Classification of Remote Sensing Images With the Maximal Margin Principle","volume":"46","author":"Blanzieri","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1778","DOI":"10.1109\/TGRS.2004.831865","article-title":"Classification of hyperspectral remote sensing images with support vector machines","volume":"42","author":"Melgani","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_50","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_51","doi-asserted-by":"crossref","unstructured":"Wang, W., Dou, S., Jiang, Z., and Sun, L. (2018). A Fast Dense Spectral\u2013Spatial Convolution Network Framework for Hyperspectral Images Classification. Remote Sens., 10.","DOI":"10.3390\/rs10071068"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Ma, W., Yang, Q., Wu, Y., Zhao, W., and Zhang, X. (2019). Double-Branch Multi-Attention Mechanism Network for Hyperspectral Image Classification. Remote Sens., 11.","DOI":"10.3390\/rs11111307"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Cui, B., Dong, X.-M., Zhan, Q., Peng, J., and Sun, W. (2021). LiteDepthwiseNet: A Lightweight Network for Hyperspectral Image Classification. IEEE Trans. Geosci. Remote Sens., 1\u201315.","DOI":"10.1109\/TGRS.2021.3062372"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Bell, S., Zitnick, C.L., Bala, K., and Girshick, R. (2016, January 27\u201330). Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.314"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Kong, T., Yao, A., Chen, Y., and Sun, F. (2016, January 27\u201330). HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.98"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"598","DOI":"10.1109\/83.913594","article-title":"A shape- and texture-based enhanced Fisher classifier for face recognition","volume":"10","author":"Liu","year":"2001","journal-title":"IEEE Trans. Image Process."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/18\/3590\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:59:28Z","timestamp":1760165968000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/18\/3590"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,9]]},"references-count":56,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2021,9]]}},"alternative-id":["rs13183590"],"URL":"https:\/\/doi.org\/10.3390\/rs13183590","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,9]]}}}