{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T22:51:07Z","timestamp":1782946267906,"version":"3.54.5"},"reference-count":46,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2020,9,11]],"date-time":"2020-09-11T00:00:00Z","timestamp":1599782400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100006407","name":"Natural Science Foundation of Henan Province","doi-asserted-by":"publisher","award":["182300410111"],"award-info":[{"award-number":["182300410111"]}],"id":[{"id":"10.13039\/501100006407","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Research Project Fund of Henan Province","award":["192102310272"],"award-info":[{"award-number":["192102310272"]}]},{"name":"Key Research Project Fund of Institution of Higher Education in Henan Province","award":["18A420001"],"award-info":[{"award-number":["18A420001"]}]},{"name":"Henan Polytechnic University Doctoral Fund","award":["B2016-13"],"award-info":[{"award-number":["B2016-13"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Convolutional neural networks provide an ideal solution for hyperspectral image (HSI) classification. However, the classification effect is not satisfactory when limited training samples are available. Focused on \u201csmall sample\u201d hyperspectral classification, we proposed a novel 3D-2D-convolutional neural network (CNN) model named AD-HybridSN (Attention-Dense-HybridSN). In our proposed model, a dense block was used to reuse shallow features and aimed at better exploiting hierarchical spatial\u2013spectral features. Subsequent depth separable convolutional layers were used to discriminate the spatial information. Further refinement of spatial\u2013spectral features was realized by the channel attention method and spatial attention method, which were performed behind every 3D convolutional layer and every 2D convolutional layer, respectively. Experiment results indicate that our proposed model can learn more discriminative spatial\u2013spectral features using very few training data. In Indian Pines, Salinas and the University of Pavia, AD-HybridSN obtain 97.02%, 99.59% and 98.32% overall accuracy using only 5%, 1% and 1% labeled data for training, respectively, which are far better than all the contrast models.<\/jats:p>","DOI":"10.3390\/s20185191","type":"journal-article","created":{"date-parts":[[2020,9,11]],"date-time":"2020-09-11T09:05:16Z","timestamp":1599815116000},"page":"5191","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["Spatial\u2013Spectral Feature Refinement for Hyperspectral Image Classification Based on Attention-Dense 3D-2D-CNN"],"prefix":"10.3390","volume":"20","author":[{"given":"Jin","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454003, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fengyuan","family":"Wei","sequence":"additional","affiliation":[{"name":"School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454003, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fan","family":"Feng","sequence":"additional","affiliation":[{"name":"School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454003, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunyang","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454003, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,11]]},"reference":[{"key":"ref_1","first-page":"1306","article-title":"A Survey on Fine-grained Image Categorization Using Deep Convolutional Features","volume":"43","author":"Luo","year":"2017","journal-title":"Acta Autom. Sin."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully convolutional networks for semantic segmentation. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","volume":"39","author":"Ren","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.isprsjprs.2019.04.015","article-title":"Deep learning in remote sensing applications: A meta-analysis and review","volume":"152","author":"Ma","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_5","first-page":"961","article-title":"Deep Learning for Hyperspectral Imagery Classification: The State of the Art and Prospects","volume":"44","author":"Zhang","year":"2018","journal-title":"Acta Autom. Sin."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.isprsjprs.2017.11.003","article-title":"MugNet: Deep learning for hyperspectral image classification using limited samples","volume":"145","author":"Pan","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_7","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20136). Imagenet classification with deep convolutional neural networks. Proceedings of the 25th International Conference on Neural Information Processing Systems, Lake Tahoe, NV, USA."},{"key":"ref_8","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_9","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., and Weinberger, K.Q. (2017, January 25\u201326). Densely Connected Convolutional Networks. Proceedings of the 2017 IEEE Conference on Pattern Recognition and Computer Vision (CVPR), College Park, MD, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Wu, P., Cui, Z., Gan, Z., and Liu, F. (2020). Three-Dimensional ResNeXt Network Using Feature Fusion and Label Smoothing for Hyperspectral Image Classification. Sensors, 20.","DOI":"10.3390\/s20061652"},{"key":"ref_11","first-page":"7553","article-title":"DenseNet with Up-Sampling block for recognizing texts in images","volume":"32","author":"Tang","year":"2020","journal-title":"Comput. Appl."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1007\/s11063-020-10240-9","article-title":"Semantic Image Segmentation with Improved Position Attention and Feature Fusion","volume":"52","author":"Zhu","year":"2020","journal-title":"Neural Process. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"105395","DOI":"10.1016\/j.cmpb.2020.105395","article-title":"DENSE-INception U-net for medical image segmentation","volume":"192","author":"Zhang","year":"2020","journal-title":"Comput. Meth. Programs Biomed."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Mu, Y., Chen, T.-S., Ninomiya, S., and Guo, W. (2020). Intact Detection of Highly Occluded Immature Tomatoes on Plants Using Deep Learning Techniques. Sensors, 20.","DOI":"10.3390\/s20102984"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1016\/j.neucom.2018.02.110","article-title":"A fast face detection method via convolutional neural network","volume":"395","author":"Guo","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.neunet.2020.05.003","article-title":"Automated classification of cells into multiple classes in epithelial tissue of oral squamous cell carcinoma using transfer learning and convolutional neural network","volume":"128","author":"Das","year":"2020","journal-title":"Neural Networks"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Li, X., Shen, X., Zhou, Y., Wang, X., and Li, T.-Q. (2020). Classification of breast cancer histopathological images using interleaved DenseNet with SENet (IDSNet). PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0232127"},{"key":"ref_18","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_19","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_20","doi-asserted-by":"crossref","first-page":"847","DOI":"10.1109\/TGRS.2017.2755542","article-title":"Spectral-spatial 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_21","doi-asserted-by":"crossref","unstructured":"Wang, W., Dou, S., Jiang, Z., and Sun, L. (2018). A Fast Dense Spectral-Spatial Convolution Network Framework for Hyperspectral Images Classification. Remote Sens., 10.","DOI":"10.3390\/rs10071068"},{"key":"ref_22","unstructured":"Fran\u00e7ois, C. (2018). Deep Learning with Python, Posts and Telecom Press. [1st ed.]."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1109\/TGRS.2012.2200106","article-title":"Semisupervised Local Discriminant Analysis for Feature Extraction in Hyperspectral Images","volume":"51","author":"Liao","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1109\/LGRS.2008.2001282","article-title":"Limitations of Principal Components Analysis for Hyperspectral Target Recognition","volume":"5","author":"Prasad","year":"2008","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1185","DOI":"10.1109\/TGRS.2011.2165957","article-title":"Locality-Preserving Dimensionality Reduction and Classification for Hyperspectral Image Analysis","volume":"50","author":"Li","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2112","DOI":"10.1109\/TGRS.2008.916629","article-title":"Supervised classification of remotely sensed imagery using a modified k-NN technique","volume":"46","author":"Samaniego","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1368","DOI":"10.1109\/36.934070","article-title":"Best-bases feature extraction algorithms for classification of hyperspectral data","volume":"39","author":"Kumar","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1335","DOI":"10.1109\/TGRS.2004.827257","article-title":"A relative evaluation of multiclass image classification by support vector machines","volume":"42","author":"Foody","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1","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_30","doi-asserted-by":"crossref","first-page":"3368","DOI":"10.1080\/2150704X.2015.1062157","article-title":"On combining multiscale deep learning features for the classification of hyperspectral remote sensing imagery","volume":"36","author":"Zhao","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_31","first-page":"53","article-title":"Deep 3D convolutional network combined with spatial-spectral features for hyperspectral image classification","volume":"48","author":"Liu","year":"2019","journal-title":"Acta Geod. Cartogr. Sin."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Meng, Z., Li, L., Jiao, L., Feng, Z., Tang, X., and Liang, M. (2019). Fully Dense Multiscale Fusion Network for Hyperspectral Image Classification. Remote Sens., 11.","DOI":"10.3390\/rs11222718"},{"key":"ref_33","first-page":"277","article-title":"HybridSN: Exploring 3-D-2-D CNN Feature Hierarchy for Hyperspectral Image Classification","volume":"17","author":"Swalpa","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Feng, F., Wang, S., Wang, C., and Zhang, J. (2019). Learning Deep Hierarchical Spatial\u2013Spectral Features for Hyperspectral Image Classification Based on Residual 3D-2D CNN. Sensors, 19.","DOI":"10.3390\/s19235276"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201322). Squeeze-and-excitation networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., and So Kweon, I. (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_37","doi-asserted-by":"crossref","unstructured":"Wang, L., Peng, J., and Sun, W. (2019). Spatial\u2013Spectral Squeeze-and-Excitation Residual Network for Hyperspectral Image Classification. Remote Sens., 11.","DOI":"10.3390\/rs11070884"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1080\/2150704X.2019.1697001","article-title":"Hyperspectral remote sensing image classification using three-dimensional-squeeze-and-excitation-DenseNet (3D-SE-DenseNet)","volume":"11","author":"Li","year":"2020","journal-title":"Remote Sens. Lett."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"5046","DOI":"10.1109\/TGRS.2018.2805286","article-title":"Generative Adversarial Networks for Hyperspectral Image Classification","volume":"56","author":"Lin","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"3318","DOI":"10.1109\/TCYB.2019.2915094","article-title":"Generative Adversarial Networks and Conditional Random Fields for Hyperspectral Image Classification","volume":"50","author":"Zhong","year":"2020","journal-title":"IEEE T. Cybern."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"839","DOI":"10.1080\/2150704X.2017.1331053","article-title":"A semi-supervised convolutional neural network for hyperspectral image classification","volume":"8","author":"Liu","year":"2017","journal-title":"Remote Sens. Lett."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1016\/j.eswa.2019.04.006","article-title":"Hyperspectral imagery classification based on semi-supervised 3-D deep neural network and adaptive band selection","volume":"129","author":"Sellami","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Song, W., Li, S., and Li, Y. (2017, January 23\u201328). Hyperspectral images classification with hybrid deep residual network. Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA.","DOI":"10.1109\/IGARSS.2017.8127433"},{"key":"ref_44","first-page":"1","article-title":"Deep Few-Shot Learning for Hyperspectral Image Classification","volume":"99","author":"Liu","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.neucom.2020.02.101","article-title":"Fine-grained vehicle type detection and recognition based on dense attention network","volume":"399","author":"Ke","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.asoc.2020.106297","article-title":"DenseAttentionSeg: Segment hands from interacted objects using depth input","volume":"92","author":"Bo","year":"2020","journal-title":"Appl. Soft. Comput."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/18\/5191\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:09:13Z","timestamp":1760177353000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/18\/5191"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,11]]},"references-count":46,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2020,9]]}},"alternative-id":["s20185191"],"URL":"https:\/\/doi.org\/10.3390\/s20185191","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,11]]}}}