{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,18]],"date-time":"2026-07-18T01:32:20Z","timestamp":1784338340673,"version":"3.55.0"},"reference-count":54,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2021,12,3]],"date-time":"2021-12-03T00:00:00Z","timestamp":1638489600000},"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":["31971789"],"award-info":[{"award-number":["31971789"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Hyperspectral images (HSIs) have been widely used in many fields of application, but it is still extremely challenging to obtain higher classification accuracy, especially when facing a smaller number of training samples in practical applications. It is very time-consuming and laborious to acquire enough labeled samples. Consequently, an efficient hybrid dense network was proposed based on a dual-attention mechanism, due to limited training samples and unsatisfactory classification accuracy. The stacked autoencoder was first used to reduce the dimensions of HSIs. A hybrid dense network framework with two feature-extraction branches was then established in order to extract abundant spectral\u2013spatial features from HSIs, based on the 3D and 2D convolutional neural network models. In addition, spatial attention and channel attention were jointly introduced in order to achieve selective learning of features derived from HSIs. The feature maps were further refined, and more important features could be retained. To improve computational efficiency and prevent the overfitting, the batch normalization layer and the dropout layer were adopted. The Indian Pines, Pavia University, and Salinas datasets were selected to evaluate the classification performance; 5%, 1%, and 1% of classes were randomly selected as training samples, respectively. In comparison with the REF-SVM, 3D-CNN, HybridSN, SSRN, and R-HybridSN, the overall accuracy of our proposed method could still reach 96.80%, 98.28%, and 98.85%, respectively. Our results show that this method can achieve a satisfactory classification performance even in the case of fewer training samples.<\/jats:p>","DOI":"10.3390\/rs13234921","type":"journal-article","created":{"date-parts":[[2021,12,6]],"date-time":"2021-12-06T03:10:38Z","timestamp":1638760238000},"page":"4921","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Hybrid Dense Network with Dual Attention for Hyperspectral Image Classification"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8352-7689","authenticated-orcid":false,"given":"Jinling","family":"Zhao","sequence":"first","affiliation":[{"name":"National Engineering Research Center for Analysis and Application of Agro-Ecological Big Data, Anhui University, Hefei 230601, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Hu","sequence":"additional","affiliation":[{"name":"National Engineering Research Center for Analysis and Application of Agro-Ecological Big Data, Anhui University, Hefei 230601, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yingying","family":"Dong","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Linsheng","family":"Huang","sequence":"additional","affiliation":[{"name":"National Engineering Research Center for Analysis and Application of Agro-Ecological Big Data, Anhui University, Hefei 230601, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1579","DOI":"10.1109\/TGRS.2017.2765364","article-title":"Recent advances on spectral-spatial 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":"2406","DOI":"10.1109\/TCYB.2018.2810806","article-title":"Feature learning using spatial-spectral hypergraph discriminant analysis for hyperspectral image","volume":"49","author":"Luo","year":"2019","journal-title":"IEEE Trans. Cybern."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"026028","DOI":"10.1117\/1.JRS.12.026028","article-title":"Deep convolutional recurrent neural network with transfer learning for hyperspectral image classification","volume":"12","author":"Liu","year":"2018","journal-title":"J. Appl. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"6248","DOI":"10.1080\/01431161.2020.1736732","article-title":"Feature extraction for hyperspectral image classification: A review","volume":"41","author":"Kumar","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"5813","DOI":"10.1109\/TGRS.2019.2902568","article-title":"Hyperspectral classification based on lightweight 3-D-CNN with transfer learning","volume":"57","author":"Zhang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","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_7","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":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_8","first-page":"447","article-title":"Classification of hyperspectral images of small samples based on support vector machine and back propagation neural network","volume":"32","author":"Fu","year":"2020","journal-title":"Sens. Mater."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1002\/wics.101","article-title":"Principal component analysis","volume":"2","author":"Abdi","year":"2010","journal-title":"Wiley Interdiscip. Rev. Comput. Stat."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1259","DOI":"10.1109\/LGRS.2019.2894470","article-title":"Linear discriminant analysis based on kernel-based possibilistic c-means for hyperspectral images","volume":"16","author":"Hou","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"036507","DOI":"10.1117\/1.JRS.14.036507","article-title":"Randomized independent component analysis and linear discriminant analysis dimensionality reduction methods for hyperspectral image classification","volume":"14","author":"Jayaprakash","year":"2020","journal-title":"J. Appl. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1017","DOI":"10.1109\/TCYB.2016.2536638","article-title":"Stacked convolutional denoising auto-encoders for feature representation","volume":"47","author":"Du","year":"2017","journal-title":"IEEE Trans. Cybern."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Khotimah, W.N., Bennamoun, M., Boussaid, F., Sohel, F., and Edwards, D. (2020). A high-performance spectral-spatial residual network for hyperspectral image classification with small training data. Remote Sens., 12.","DOI":"10.3390\/rs12193137"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.infrared.2019.04.007","article-title":"Non-destructive classification of defective potatoes based on hyperspectral imaging and support vector machine","volume":"99","author":"Ji","year":"2019","journal-title":"Infrared Phys. Technol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1007\/s11554-018-0793-9","article-title":"Fast dimensionality reduction and classification of hyperspectral images with extreme learning machines","volume":"15","author":"Plaza","year":"2018","journal-title":"J. Real-Time Image Process."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1177\/0003702815620545","article-title":"Random Forest (RF) wrappers for waveband selection and classification of hyperspectral data","volume":"70","author":"Poona","year":"2016","journal-title":"Appl. Spectrosc."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2020.2991290","article-title":"A novel fast single image dehazing algorithm based on artificial multiexposure image fusion","volume":"70","author":"Zhu","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Han, Y., Yin, M., Duan, P., and Ghamisi, P. (2021). Edge-preserving filtering-based dehazing for remote sensing images. IEEE Geosci. Remote Sens. Lett., 1\u20135.","DOI":"10.1109\/LGRS.2021.3103381"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1109\/MGRS.2019.2912563","article-title":"Deep learning for classification of hyperspectral data","volume":"7","author":"Audebert","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.neucom.2019.10.008","article-title":"Impact of fully connected layers on performance of convolutional neural networks for image classification","volume":"378","author":"Basha","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"4417","DOI":"10.1007\/s11042-017-4734-6","article-title":"Hyperspectral data classification based on flexible momentum deep convolution neural network","volume":"77","author":"Yue","year":"2018","journal-title":"Multimed. Tools Appl."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3459","DOI":"10.1080\/01431161.2015.1055607","article-title":"Hyperspectral classification via deep networks and superpixel segmentation","volume":"36","author":"Liu","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2094","DOI":"10.1109\/JSTARS.2014.2329330","article-title":"Deep learning-based classification of hyperspectral data","volume":"7","author":"Chen","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2381","DOI":"10.1109\/JSTARS.2015.2388577","article-title":"Spectral-spatial classification of hyperspectral data based on deep belief network","volume":"8","author":"Chen","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"4544","DOI":"10.1109\/TGRS.2016.2543748","article-title":"Spectral-spatial feature extraction for hyperspectral image classification: A dimension reduction and deep learning approach","volume":"54","author":"Zhao","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","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_27","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1109\/LGRS.2019.2918719","article-title":"HybridSN: Exploring 3-D-2-D CNN feature hierarchy for hyperspectral image classification","volume":"17","author":"Roy","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1166","DOI":"10.1109\/JSTARS.2017.2767185","article-title":"Classification of hyperspectral images by Gabor filtering based deep network","volume":"11","author":"Kang","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1859","DOI":"10.1007\/s11042-020-09480-7","article-title":"Hyperspectral remote sensing image classification based on dense residual three-dimensional convolutional neural network","volume":"80","author":"Chen","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref_30","first-page":"463","article-title":"A hybrid deep ResNet and inception model for hyperspectral image classification","volume":"88","author":"Alotaibi","year":"2020","journal-title":"PFG J. Photogramm. Remote Sens. Geoinf. Sci."},{"key":"ref_31","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_32","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_33","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_34","doi-asserted-by":"crossref","unstructured":"Feng, F., Wang, S., Wang, C., and Zhang, J. (2019). Learning deep hierarchical spatial-spectral features for hyperspectral image classification based on residual 3D-2D CNN. Sensors, 19.","DOI":"10.3390\/s19235276"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.neucom.2019.09.012","article-title":"Combining attention-based bidirectional gated recurrent neural network and two-dimensional convolutional neural network for document-level sentiment classification","volume":"371","author":"Liu","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"8065","DOI":"10.1109\/TGRS.2019.2918080","article-title":"Visual attention-driven hyperspectral image classification","volume":"57","author":"Haut","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Fang, B., Li, Y., Zhang, H., and Chan, J.C.-W. (2019). Hyperspectral images classification based on dense convolutional networks with spectral-wise attention mechanism. Remote Sens., 11.","DOI":"10.3390\/rs11020159"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Mei, X., Pan, E., Ma, Y., Dai, X., Huang, J., Fan, F., Du, Q., Zheng, H., and Ma, J. (2019). Spectral-spatial attention networks for hyperspectral image classification. Remote Sens., 11.","DOI":"10.3390\/rs11080963"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Zhu, Z., Luo, Y., Qi, G., Meng, J., Li, Y., and Mazur, N. (2021). Remote sensing image defogging networks based on dual self-attention boost residual octave convolution. Remote Sens., 13.","DOI":"10.3390\/rs13163104"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"3232","DOI":"10.1109\/TGRS.2019.2951160","article-title":"Spectral-spatial attention network for hyperspectral image classification","volume":"58","author":"Sun","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"3223","DOI":"10.1080\/01431160152558332","article-title":"A generalized confusion matrix for assessing area estimates from remotely sensed data","volume":"22","author":"Lewis","year":"2001","journal-title":"Int. J. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"116111","DOI":"10.1016\/j.image.2020.116111","article-title":"Hyperspectral image classification using an extended Auto-Encoder method","volume":"92","author":"Ghasrodashti","year":"2021","journal-title":"Signal Process. Image Commun."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"103280","DOI":"10.1016\/j.micpro.2020.103280","article-title":"Auto encoder based dimensionality reduction and classification using convolutional neural networks for hyperspectral images","volume":"79","author":"Ramamurthy","year":"2020","journal-title":"Microprocess. Microsyst."},{"key":"ref_44","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. Available online: https:\/\/doi.org\/10.1109\/CVPR.2016.90.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., and Weinberger, K.Q. (2017, January 21\u201326). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA. Available online: https:\/\/doi.org\/10.1109\/CVPR.2017.243.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1750","DOI":"10.1109\/TMM.2018.2889562","article-title":"Predicting stereoscopic image quality via stacked auto-encoders based on stereopsis formation","volume":"21","author":"Yang","year":"2019","journal-title":"IEEE Trans. Multimed."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"3862","DOI":"10.1109\/JSTARS.2020.3006241","article-title":"An augmentation attention mechanism for high-spatial-resolution remote sensing image scene classification","volume":"13","author":"Li","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Xu, R., Tao, Y., Lu, Z., and Zhong, Y. (2018). Attention-mechanism-containing neural networks for high-resolution remote sensing image classification. Remote Sens., 10.","DOI":"10.3390\/rs10101602"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Zhao, X., Zhang, J., Tian, J., Zhuo, L., and Zhang, J. (2020). Residual dense network based on channel-spatial attention for the scene classification of a high-resolution remote sensing image. Remote Sens., 12.","DOI":"10.3390\/rs12111887"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Xu, Q., Xiao, Y., Wang, D., and Luo, B. (2020). CSA-MSO3DCNN: Multiscale octave 3D CNN with channel and spatial attention for hyperspectral image classification. Remote Sens., 12.","DOI":"10.3390\/rs12010188"},{"key":"ref_51","first-page":"5893","article-title":"Spectral-spatial unified networks for hyperspectral image classification","volume":"56","author":"Xu","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"i121","DOI":"10.1093\/bioinformatics\/btw255","article-title":"Convolutional neural network architectures for predicting DNA\u2013protein binding","volume":"3","author":"Zeng","year":"2016","journal-title":"Bioinformatics"},{"key":"ref_53","first-page":"1","article-title":"Convolutional neural network with automatic learning rate scheduler for fault classification","volume":"70","author":"Wen","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"12777","DOI":"10.1007\/s11042-019-08453-9","article-title":"Dropout vs. batch normalization: An empirical study of their impact to deep learning","volume":"79","author":"Garbin","year":"2020","journal-title":"Multimed. Tools Appl."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/23\/4921\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:39:31Z","timestamp":1760168371000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/23\/4921"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,3]]},"references-count":54,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["rs13234921"],"URL":"https:\/\/doi.org\/10.3390\/rs13234921","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12,3]]}}}