{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T21:27:14Z","timestamp":1782422834537,"version":"3.54.5"},"reference-count":51,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2021,5,17]],"date-time":"2021-05-17T00:00:00Z","timestamp":1621209600000},"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>In recent years, with the rapid development of computer vision, increasing attention has been paid to remote sensing image scene classification. To improve the classification performance, many studies have increased the depth of convolutional neural networks (CNNs) and expanded the width of the network to extract more deep features, thereby increasing the complexity of the model. To solve this problem, in this paper, we propose a lightweight convolutional neural network based on attention-oriented multi-branch feature fusion (AMB-CNN) for remote sensing image scene classification. Firstly, we propose two convolution combination modules for feature extraction, through which the deep features of images can be fully extracted with multi convolution cooperation. Then, the weights of the feature are calculated, and the extracted deep features are sent to the attention mechanism for further feature extraction. Next, all of the extracted features are fused by multiple branches. Finally, depth separable convolution and asymmetric convolution are implemented to greatly reduce the number of parameters. The experimental results show that, compared with some state-of-the-art methods, the proposed method still has a great advantage in classification accuracy with very few parameters.<\/jats:p>","DOI":"10.3390\/rs13101950","type":"journal-article","created":{"date-parts":[[2021,5,17]],"date-time":"2021-05-17T12:19:57Z","timestamp":1621253997000},"page":"1950","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":47,"title":["A Multi-Branch Feature Fusion Strategy Based on an Attention Mechanism for Remote Sensing Image Scene Classification"],"prefix":"10.3390","volume":"13","author":[{"given":"Cuiping","family":"Shi","sequence":"first","affiliation":[{"name":"College of Communication and Electronic Engineering, Qiqihar University, Qiqihar 161000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Communication and Electronic Engineering, Qiqihar University, Qiqihar 161000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liguo","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Information and Communication Engineering, Dalian Nationalities University, Dalian 116000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4799","DOI":"10.1109\/TGRS.2019.2893115","article-title":"A Deep Scene Representation for Aerial Scene Classification","volume":"57","author":"Zheng","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1779","DOI":"10.1109\/TGRS.2018.2869101","article-title":"Remote sensing image scene classification using rearranged local features","volume":"57","author":"Yuan","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"5599","DOI":"10.1109\/JSTARS.2016.2615125","article-title":"An Inversion-Based Fusion Method for Inland Water Remote Monitoring","volume":"9","author":"Guo","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"8358","DOI":"10.1109\/TGRS.2020.2987338","article-title":"Attribute-Cooperated Convolutional Neural Network for Remote Sensing Image Classification","volume":"58","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"5653","DOI":"10.1109\/TGRS.2017.2711275","article-title":"Integrating multilayer features of convolutional neural networks for remote sensing scene classification","volume":"55","author":"Li","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"6521","DOI":"10.1109\/TGRS.2018.2839705","article-title":"Learning Source-Invariant Deep Hashing Convolutional Neural Networks for Cross-Source Remote Sensing Image Retrieval","volume":"56","author":"Li","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","article-title":"Distinctive Image Features from Scale-Invariant Keypoints","volume":"60","author":"Lowe","year":"2004","journal-title":"Int. J. Comput. Vis."},{"key":"ref_8","unstructured":"Laurent, I., Geraint, R., and John, K.T. (2005). Chapter 41\u2014Gist of the Scene. Neurobiology of Attention, Academic Press."},{"key":"ref_9","unstructured":"Dalal, N., and Triggs, B. (2005, January 20\u201325). Histograms of oriented gradients for human detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Diego, CA, USA."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Gamage, P.T., Azad, M.K., Taebi, A., Sandler, R.H., and Mansy, H.A. (2018, January 1). Clustering Seismocardiographic Events using Unsupervised Machine Learning. Proceedings of the 2018 IEEE Signal Processing in Medicine and Biology Symposium (SPMB), Philadelphia, PA, USA.","DOI":"10.1109\/SPMB.2018.8615615"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1521","DOI":"10.1109\/JSTARS.2015.2513898","article-title":"Unsupervised Quaternion Feature Learning for Remote Sensing Image Classification","volume":"9","author":"Risojevic","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. 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","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"Lecun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_14","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2012","journal-title":"Commun. ACM"},{"key":"ref_15","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_16","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 Proceedings of the IEEE conference on computer vision and pattern recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1109\/LGRS.2017.2779469","article-title":"Scene classification based on two-stage deep feature fusion","volume":"15","author":"Liu","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"4775","DOI":"10.1109\/TGRS.2017.2700322","article-title":"Deep Feature Fusion for VHR Remote Sensing Scene Classification","volume":"55","author":"Chaib","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2811","DOI":"10.1109\/TGRS.2017.2783902","article-title":"When deep learning meets metric learning: Remote sensing image scene classification via learning discriminative CNNs","volume":"56","author":"Cheng","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","first-page":"1","article-title":"A novel two-stage scene classification model based on feature variable significance in high-resolution remote sensing","volume":"35","author":"Zhao","year":"2019","journal-title":"Geocarto Int."},{"key":"ref_21","unstructured":"Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Dally, W.J., and Keutzer, K. (2017, January 24\u201326). SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5 MB model size. Proceedings of the 5th International Conference on Learning Representations, ICLR 2017, Toulon, France."},{"key":"ref_22","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., and Weyand, T. (2017). MobileNets: Efficient convolutional neural networks for mobile vision applications. arXiv Prepr."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhang, W., Tang, P., and Zhao, L. (2019). Remote sensing image scene classification using CNN-CapsNet. Remote Sens., 11.","DOI":"10.3390\/rs11050494"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2738","DOI":"10.1109\/JSTARS.2020.2997081","article-title":"Attention Receptive Pyramid Network for Ship Detection in SAR Images","volume":"13","author":"Zhao","year":"2020","journal-title":"IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"155441","DOI":"10.1109\/ACCESS.2020.3018784","article-title":"FDTA: Fully Convolutional Scene Text Detection with Text Attention","volume":"8","author":"Cao","year":"2020","journal-title":"IEEE Access"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1985","DOI":"10.1109\/TGRS.2019.2951636","article-title":"Sound Active Attention Framework for Remote Sensing Image Captioning","volume":"58","author":"Lu","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","unstructured":"He, X., Haffari, G., and Norouzi, M. (November, January 31). Sequence to Sequence Mixture Model for Diverse Machine Translation. Proceedings of the 22nd Conference on Computational Natural Language Learning, Brussels, Belgium."},{"key":"ref_28","unstructured":"Lin, Z., Feng, M., dos Santos, C.N., Yu, M., Xiang, B., Zhou, B., and Bengio, Y. (2017, January 24\u201326). A structured self-attentive sentence embedding. Proceedings of the 5th International Conference on Learning Representations, ICLR 2017, Toulon, France."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1155","DOI":"10.1109\/TGRS.2018.2864987","article-title":"Scene Classification With Recurrent Attention of VHR Remote Sensing Images","volume":"57","author":"Wang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"4121","DOI":"10.1109\/JSTARS.2020.3009352","article-title":"Channel-Attention-Based DenseNet Network for Remote Sensing Image Scene Classification","volume":"13","author":"Tong","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"6372","DOI":"10.1109\/JSTARS.2020.3030257","article-title":"Hierarchical Attention and Bilinear Fusion for Remote Sensing Image Scene Classification","volume":"13","author":"Yu","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"14078","DOI":"10.1109\/ACCESS.2021.3051085","article-title":"Classification of Remote Sensing Images Using EfficientNet-B3 CNN Model With Attention","volume":"9","author":"Alhichri","year":"2021","journal-title":"IEEE Access"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"54135","DOI":"10.1109\/ACCESS.2020.2981358","article-title":"Semi-supervised representation learning for remote sensing image classification based on generative adversarial networks","volume":"8","author":"Yan","year":"2020","journal-title":"IEEE Access"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"6884","DOI":"10.1080\/01431161.2019.1597302","article-title":"Multiple resolution block feature for remote-sensing scene classification","volume":"40","author":"Wang","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1200","DOI":"10.1109\/LGRS.2019.2894399","article-title":"Siamese convolutional neural networks for remote sensing scene classification","volume":"16","author":"Liu","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1186\/s13640-018-0398-z","article-title":"Remote sensing scene classification based on rotation-invariant feature learning and joint decision making","volume":"2019","author":"Zhou","year":"2019","journal-title":"EURASIP J. Image Video Process."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.neucom.2018.03.076","article-title":"Bidirectional adaptive feature fusion for remote sensing scene classification","volume":"328","author":"Lu","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"7109","DOI":"10.1109\/TGRS.2018.2848473","article-title":"Scene Classification Based on Multiscale Convolutional Neural Network","volume":"56","author":"Liu","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1109\/LGRS.2020.2968550","article-title":"Self-Attention-Based Deep Feature Fusion for Remote Sensing Scene Classification","volume":"18","author":"Cao","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Liu, B.D., Meng, J., Xie, W.Y., Shao, S., Li, Y., and Wang, Y. (2019). Weighted spatial pyramid matching collaborative representation for remote-sensing-image scene classification. Remote Sens., 11.","DOI":"10.3390\/rs11050518"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1461","DOI":"10.1109\/TNNLS.2019.2920374","article-title":"Skip-connected covariance network for remote sensing scene classification","volume":"31","author":"He","year":"2020","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"6899","DOI":"10.1109\/TGRS.2018.2845668","article-title":"Remote sensing scene classification using multilayer stacked covariance pooling","volume":"56","author":"He","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1109\/TGRS.2019.2931801","article-title":"Remote sensing scene classification by gated bidirectional network","volume":"58","author":"Sun","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"7894","DOI":"10.1109\/TGRS.2019.2917161","article-title":"A Feature Aggregation Convolutional Neural Network for Remote Sensing Scene Classification","volume":"57","author":"Lu","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"3508","DOI":"10.1109\/JSTARS.2019.2934165","article-title":"Aggregated deep fisher feature for VHR remote sensing scene classification","volume":"12","author":"Lietal","year":"2019","journal-title":"IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1944","DOI":"10.1109\/LGRS.2019.2911855","article-title":"Remote Sensing Scene Classification Using Convolutional Features and Deep Forest Classifier","volume":"16","author":"Boualleg","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"6916","DOI":"10.1109\/TGRS.2019.2909695","article-title":"Scale-free convolutional neural network for remote sensing scene classification","volume":"57","author":"Xie","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"943","DOI":"10.1109\/LGRS.2019.2937811","article-title":"Positional Context Aggregation Network for Remote Sensing Scene Classification","volume":"17","author":"Zhang","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"5194","DOI":"10.1109\/JSTARS.2020.3018307","article-title":"Branch Feature Fusion Convolution Network for Remote Sensing Scene Classification","volume":"13","author":"Shi","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Li, J., Lin, D., Wang, Y., Xu, G., Zhang, Y., Ding, C., and Zhou, Y. (2020). Deep Discriminative Representation Learning with Attention Map for Scene Classification. Remote Sens., 12.","DOI":"10.3390\/rs12091366"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"3965","DOI":"10.1109\/TGRS.2017.2685945","article-title":"AID: A Benchmark Data Set for Performance Evaluation of Aerial Scene Classification","volume":"55","author":"Xia","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/10\/1950\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:02:40Z","timestamp":1760162560000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/10\/1950"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,17]]},"references-count":51,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2021,5]]}},"alternative-id":["rs13101950"],"URL":"https:\/\/doi.org\/10.3390\/rs13101950","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,5,17]]}}}