{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T15:32:09Z","timestamp":1781019129266,"version":"3.54.1"},"reference-count":149,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2021,7,28]],"date-time":"2021-07-28T00:00:00Z","timestamp":1627430400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Machine learning, particularly deep learning (DL), has become a central and state-of-the-art method for several computer vision applications and remote sensing (RS) image processing. Researchers are continually trying to improve the performance of the DL methods by developing new architectural designs of the networks and\/or developing new techniques, such as attention mechanisms. Since the attention mechanism has been proposed, regardless of its type, it has been increasingly used for diverse RS applications to improve the performances of the existing DL methods. However, these methods are scattered over different studies impeding the selection and application of the feasible approaches. This study provides an overview of the developed attention mechanisms and how to integrate them with different deep learning neural network architectures. In addition, it aims to investigate the effect of the attention mechanism on deep learning-based RS image processing. We identified and analyzed the advances in the corresponding attention mechanism-based deep learning (At-DL) methods. A systematic literature review was performed to identify the trends in publications, publishers, improved DL methods, data types used, attention types used, overall accuracies achieved using At-DL methods, and extracted the current research directions, weaknesses, and open problems to provide insights and recommendations for future studies. For this, five main research questions were formulated to extract the required data and information from the literature. Furthermore, we categorized the papers regarding the addressed RS image processing tasks (e.g., image classification, object detection, and change detection) and discussed the results within each group. In total, 270 papers were retrieved, of which 176 papers were selected according to the defined exclusion criteria for further analysis and detailed review. The results reveal that most of the papers reported an increase in overall accuracy when using the attention mechanism within the DL methods for image classification, image segmentation, change detection, and object detection using remote sensing images.<\/jats:p>","DOI":"10.3390\/rs13152965","type":"journal-article","created":{"date-parts":[[2021,7,28]],"date-time":"2021-07-28T05:23:48Z","timestamp":1627449828000},"page":"2965","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":193,"title":["Effect of Attention Mechanism in Deep Learning-Based Remote Sensing Image Processing: A Systematic Literature Review"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9882-4603","authenticated-orcid":false,"given":"Saman","family":"Ghaffarian","sequence":"first","affiliation":[{"name":"Information Technology Group, Wageningen University & Research, 6707 KN Wageningen, The Netherlands"},{"name":"Business Economics Group, Wageningen University & Research, 6700 EW Wageningen, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6241-4124","authenticated-orcid":false,"given":"Jo\u00e3o","family":"Valente","sequence":"additional","affiliation":[{"name":"Information Technology Group, Wageningen University & Research, 6707 KN Wageningen, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mariska","family":"van der Voort","sequence":"additional","affiliation":[{"name":"Business Economics Group, Wageningen University & Research, 6700 EW Wageningen, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8538-7261","authenticated-orcid":false,"given":"Bedir","family":"Tekinerdogan","sequence":"additional","affiliation":[{"name":"Information Technology Group, Wageningen University & Research, 6707 KN Wageningen, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"111402","DOI":"10.1016\/j.rse.2019.111402","article-title":"Remote sensing for agricultural applications: A meta-review","volume":"236","author":"Weiss","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1217","DOI":"10.1080\/01431161.2018.1524178","article-title":"An improved cluster-based snake model for automatic agricultural field boundary extraction from high spatial resolution imagery","volume":"40","author":"Ghaffarian","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1366","DOI":"10.1007\/s11119-020-09725-3","article-title":"Automated crop plant counting from very high-resolution aerial imagery","volume":"21","author":"Valente","year":"2020","journal-title":"Precis. Agric."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Zhang, C., Valente, J., Kooistra, L., Guo, L., and Wang, W. (2021). Orchard management with small unmanned aerial vehicles: A survey of sensing and analysis approaches. Precis. Agric.","DOI":"10.1007\/s11119-021-09813-y"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.compenvurbsys.2015.02.002","article-title":"Remote sensing for urban planning and management: The use of window-independent context segmentation to extract urban features in Stockholm","volume":"52","author":"Nielsen","year":"2015","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1007\/s41207-016-0007-4","article-title":"Advances in remote sensing applications for urban sustainability","volume":"1","author":"Kadhim","year":"2016","journal-title":"Euro-Mediterr. J. Environ. Integr."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.isprsjprs.2014.08.017","article-title":"Automatic building detection based on Purposive FastICA (PFICA) algorithm using monocular high resolution Google Earth images","volume":"97","author":"Ghaffarian","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Ghaffarian, S., Kerle, N., and Filatova, T. (2018). Remote Sensing-Based Proxies for Urban Disaster Risk Management and Resilience: A Review. Remote Sens., 10.","DOI":"10.3390\/rs10111760"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Ghaffarian, S., Rezaie Farhadabad, A., and Kerle, N. (2020). Post-Disaster Recovery Monitoring with Google Earth Engine. Appl. Sci., 10.","DOI":"10.3390\/app10134574"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Ghaffarian, S., and Emtehani, S. (2021). Monitoring Urban Deprived Areas with Remote Sensing and Machine Learning in Case of Disaster Recovery. Climate, 9.","DOI":"10.3390\/cli9040058"},{"key":"ref_11","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_12","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","article-title":"Random forest in remote sensing: A review of applications and future directions","volume":"114","author":"Belgiu","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"6308","DOI":"10.1109\/JSTARS.2020.3026724","article-title":"Support Vector Machine Versus Random Forest for Remote Sensing Image Classification: A Meta-Analysis and Systematic Review","volume":"13","author":"Sheykhmousa","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1109\/MGRS.2016.2540798","article-title":"Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art","volume":"4","author":"Zhang","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"e1264","DOI":"10.1002\/widm.1264","article-title":"Deep learning for remote sensing image classification: A survey","volume":"8","author":"Li","year":"2018","journal-title":"WIREs Data Min. Knowl. Discov."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2020.12.010","article-title":"Review on Convolutional Neural Networks (CNN) in vegetation remote sensing","volume":"173","author":"Kattenborn","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3602","DOI":"10.1109\/JSTARS.2021.3065569","article-title":"A Meta-Analysis of Convolutional Neural Networks for Remote Sensing Applications","volume":"14","author":"Ghanbari","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Liu, X., Wang, Y., and Liu, Q. (2018, January 7\u201310). Psgan: A Generative Adversarial Network for Remote Sensing Image Pan-Sharpening. Proceedings of the 2018 25th IEEE International Conference on Image Processing (ICIP), Athens, Greece.","DOI":"10.1109\/ICIP.2018.8451049"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/j.isprsjprs.2019.02.010","article-title":"A graph convolutional neural network for classification of building patterns using spatial vector data","volume":"150","author":"Yan","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_20","unstructured":"Bahdanau, D., Cho, K., and Bengio, Y. (2014). Neural machine translation by jointly learning to align and translate. arXiv."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Niu, Z., Zhong, G., and Yu, H. (2021). A Review on the Attention Mechanism of Deep Learning. Neurocomputing.","DOI":"10.1016\/j.neucom.2021.03.091"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zhang, J., Zhou, Q., Wu, J., Wang, Y.C., Wang, H., Li, Y.S., Chai, Y.Z., and Liu, Y. (2020). A Cloud Detection Method Using Convolutional Neural Network Based on Gabor Transform and Attention Mechanism with Dark Channel Subnet for Remote Sensing Image. Remote Sens., 12.","DOI":"10.3390\/rs12193261"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zeng, Y.L., Ritz, C., Zhao, J.H., and Lan, J.H. (2020). Attention-Based Residual Network with Scattering Transform Features for Hyperspectral Unmixing with Limited Training Samples. Remote Sens., 12.","DOI":"10.3390\/rs12030400"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"519","DOI":"10.1109\/TGRS.2019.2937830","article-title":"Attention GANs: Unsupervised Deep Feature Learning for Aerial Scene Classification","volume":"58","author":"Yu","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Gao, F., He, Y.S., Wang, J., Hussain, A., and Zhou, H.Y. (2020). Anchor-free Convolutional Network with Dense Attention Feature Aggregation for Ship Detection in SAR Images. Remote Sens., 12.","DOI":"10.3390\/rs12162619"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"8077","DOI":"10.1109\/TGRS.2020.2987060","article-title":"High-Resolution Remote Sensing Image Scene Classification via Key Filter Bank Based on Convolutional Neural Network","volume":"58","author":"Li","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Yang, H., Wu, P.H., Yao, X.D., Wu, Y.L., Wang, B., and Xu, Y.Y. (2018). Building Extraction in Very High Resolution Imagery by Dense-Attention Networks. Remote Sens., 10.","DOI":"10.3390\/rs10111768"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MGRS.2017.2762307","article-title":"Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources","volume":"5","author":"Zhu","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.inffus.2016.05.004","article-title":"Pixel-level image fusion: A survey of the state of the art","volume":"33","author":"Li","year":"2017","journal-title":"Inf. Fusion"},{"key":"ref_30","unstructured":"Galassi, A., Lippi, M., and Torroni, P. (2020). Attention in Natural Language Processing. IEEE Trans. Neural Netw. Learn. Syst., 1\u201318."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Ko\u0161\u010devi\u0107, K., Suba\u0161i\u0107, M., and Lon\u010dari\u0107, S. (2019, January 23\u201325). Attention-based Convolutional Neural Network for Computer Vision Color Constancy. Proceedings of the 2019 11th International Symposium on Image and Signal Processing and Analysis (ISPA), Dubrovnik, Croatia.","DOI":"10.1109\/ISPA.2019.8868806"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"11307","DOI":"10.1038\/s41598-020-67529-x","article-title":"Object detection based on an adaptive attention mechanism","volume":"10","author":"Li","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Cui, W., Wang, F., He, X., Zhang, D.Y., Xu, X.X., Yao, M., Wang, Z.W., and Huang, J.J. (2019). Multi-Scale Semantic Segmentation and Spatial Relationship Recognition of Remote Sensing Images Based on an Attention Model. Remote Sens., 11.","DOI":"10.3390\/rs11091044"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"119873","DOI":"10.1109\/ACCESS.2019.2936616","article-title":"Deep Attention Neural Network for Multi-Label Classification in Unmanned Aerial Vehicle Imagery","volume":"7","author":"Alshehri","year":"2019","journal-title":"IEEE Access"},{"key":"ref_35","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_36","doi-asserted-by":"crossref","unstructured":"Feng, J., Wu, X., Shang, R., Sui, C., Li, J., Jiao, L., and Zhang, X. (2020). Attention Multibranch Convolutional Neural Network for Hyperspectral Image Classification Based on Adaptive Region Search. IEEE Trans. Geosci. Remote Sens.","DOI":"10.1109\/TGRS.2020.3011943"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Zhao, Z., Wang, H., and Yu, X. (2021). Spectral-Spatial Graph Attention Network for Semisupervised Hyperspectral Image Classification. IEEE Geosci. Remote Sens. Lett.","DOI":"10.1109\/LGRS.2021.3059509"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"23070","DOI":"10.1109\/ACCESS.2021.3055554","article-title":"Attentive Spatial Temporal Graph CNN for Land Cover Mapping from Multi Temporal Remote Sensing Data","volume":"9","author":"Censi","year":"2021","journal-title":"IEEE Access"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201323). Squeeze-and-Excitation Networks. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_40","unstructured":"Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A., Salakhudinov, R., Zemel, R., and Bengio, Y. (2015, January 7\u20139). Show, Attend and Tell: Neural Image Caption Generation with Visual Attention. Proceedings of the 32nd International Conference on Machine Learning, Proceedings of Machine Learning Research, Lille, France."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"67200","DOI":"10.1109\/ACCESS.2019.2918732","article-title":"Global-Local Attention Network for Aerial Scene Classification","volume":"7","author":"Guo","year":"2019","journal-title":"IEEE Access"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Ma, J., Ma, Q., Tang, X., Zhang, X., Zhu, C., Peng, Q., and Jiao, L. (2020, January 11\u201316). Remote Sensing Scene Classification Based on Global and Local Consistent Network. Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS), Brussels, Belgium.","DOI":"10.1109\/IGARSS39084.2020.9323281"},{"key":"ref_43","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_44","doi-asserted-by":"crossref","unstructured":"Wang, L., Peng, J.T., and Sun, W.W. (2019). Spatial-Spectral Squeeze-and-Excitation Residual Network for Hyperspectral Image Classification. Remote Sens., 11.","DOI":"10.3390\/rs11070884"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Alswayed, A.S., Alhichri, H.S., and Bazi, Y. (2020, January 19\u201321). SqueezeNet with Attention for Remote Sensing Scene Classification. Proceedings of the ICCAIS 2020\u20143rd International Conference on Computer Applications and Information Security, Riyadh, Saudi Arabia.","DOI":"10.1109\/ICCAIS48893.2020.9096876"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Li, C.Y., Luo, B., Hong, H.L., Su, X., Wang, Y.J., Liu, J., Wang, C.J., Zhang, J., and Wei, L.H. (2020). Object Detection Based on Global-Local Saliency Constraint in Aerial Images. Remote Sens., 12.","DOI":"10.3390\/rs12091435"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1109\/LGRS.2019.2924822","article-title":"Local Attention Networks for Occluded Airplane Detection in Remote Sensing Images","volume":"17","author":"Zhou","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"426","DOI":"10.1109\/TGRS.2020.2994150","article-title":"LANet: Local Attention Embedding to Improve the Semantic Segmentation of Remote Sensing Images","volume":"59","author":"Ding","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_49","unstructured":"Chaudhari, S., Mithal, V., Polatkan, G., and Ramanath, R. (2019). An attentive survey of attention models. arXiv."},{"key":"ref_50","unstructured":"Lu, J., Yang, J., Batra, D., and Parikh, D. (2016, January 5\u201310). Hierarchical question-image co-attention for visual question answering. Proceedings of the NIPS, Barcelona, Spain."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Jiang, H.W., Hu, X.Y., Li, K., Zhang, J.M., Gong, J.Q., and Zhang, M. (2020). PGA-SiamNet: Pyramid Feature-Based Attention-Guided Siamese Network for Remote Sensing Orthoimagery Building Change Detection. Remote Sens., 12.","DOI":"10.3390\/rs12030484"},{"key":"ref_52","unstructured":"He, N., Fang, L., Li, Y., and Plaza, A. (August, January 28). High-Order Self-Attention Network for Remote Sensing Scene Classification. Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS), Yokohama, Japan."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1792","DOI":"10.1109\/LGRS.2019.2955071","article-title":"Self-Attentive Generative Adversarial Network for Cloud Detection in High Resolution Remote Sensing Images","volume":"17","author":"Wu","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_54","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_55","doi-asserted-by":"crossref","first-page":"210347","DOI":"10.1109\/ACCESS.2020.3038989","article-title":"Self-Attention Network With Joint Loss for Remote Sensing Image Scene Classification","volume":"8","author":"Wu","year":"2020","journal-title":"IEEE Access"},{"key":"ref_56","unstructured":"Xiao, T., Xu, Y., Yang, K., Zhang, J., Peng, Y., and Zhang, Z. (2015, January 7\u201312). The application of two-level attention models in deep convolutional neural network for fine-grained image classification. Proceedings of the CVPR, IEEE Computer Society, Boston, MA, USA."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"4929","DOI":"10.1109\/TGRS.2019.2894425","article-title":"Multisource Region Attention Network for Fine-Grained Object Recognition in Remote Sensing Imagery","volume":"57","author":"Sumbul","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Li, J., Tu, Z., Yang, B., Lyu, M.R., and Zhang, T. (November, January 31). Multi-Head Attention with Disagreement Regularization. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium.","DOI":"10.18653\/v1\/D18-1317"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Zhang, S.Y., Li, C.R., Qiu, S., Gao, C.X., Zhang, F., Du, Z.H., and Liu, R.Y. (2020). EMMCNN: An ETPS-Based Multi-Scale and Multi-Feature Method Using CNN for High Spatial Resolution Image Land-Cover Classification. Remote Sens., 12.","DOI":"10.3390\/rs12010066"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Cheng, B., Li, Z.Z., Xu, B.T., Yao, X., Ding, Z.Q., and Qin, T.Q. (2021). Structured Object-Level Relational Reasoning CNN-Based Target Detection Algorithm in a Remote Sensing Image. Remote Sens., 13.","DOI":"10.3390\/rs13020281"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1200","DOI":"10.1109\/TGRS.2020.3004911","article-title":"Multiscale CNN with Autoencoder Regularization Joint Contextual Attention Network for SAR Image Classification","volume":"59","author":"Wu","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Shen, T., Zhou, T., Long, G., Jiang, J., Pan, S., and Zhang, C. (2018, January 2\u20137). DiSAN: Directional Self-Attention Network for RNN\/CNN-free Language Understanding. Proceedings of the AAAI, New Orleans, LA, USA.","DOI":"10.1609\/aaai.v32i1.11941"},{"key":"ref_63","unstructured":"Du, J., Han, J., Way, A., and Wan, D. (November, January 31). Multi-Level Structured Self-Attentions for Distantly Supervised Relation Extraction. Proceedings of the EMNLP, Brussels, Belgium."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1484","DOI":"10.1016\/j.visres.2011.04.012","article-title":"Visual attention: The past 25 years","volume":"51","author":"Carrasco","year":"2011","journal-title":"Vis. Res."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/j.visres.2015.04.004","article-title":"A mechanistic cortical microcircuit of attention for amplification, normalization and suppression","volume":"116","author":"Beuth","year":"2015","journal-title":"Vis. Res."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1254","DOI":"10.1109\/34.730558","article-title":"A model of saliency-based visual attention for rapid scene analysis","volume":"20","author":"Itti","year":"1998","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Ma, W.P., Zhao, J.L., Zhu, H., Shen, J.C., Jiao, L.C., Wu, Y., and Hou, B.A. (2021). A Spatial-Channel Collaborative Attention Network for Enhancement of Multiresolution Classification. Remote Sens., 13.","DOI":"10.3390\/rs13010106"},{"key":"ref_68","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_69","doi-asserted-by":"crossref","first-page":"6344","DOI":"10.1109\/ACCESS.2019.2963769","article-title":"Scene Classification of Remote Sensing Images Based on Saliency Dual Attention Residual Network","volume":"8","author":"Guo","year":"2020","journal-title":"IEEE Access"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"2281","DOI":"10.1109\/TGRS.2020.3007921","article-title":"Hyperspectral Image Classification With Attention-Aided CNNs","volume":"59","author":"Hang","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1109\/TGRS.2020.2994057","article-title":"Residual Spectral-Spatial Attention Network for Hyperspectral Image Classification","volume":"59","author":"Zhu","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Ren, Y.F., Yu, Y.T., and Guan, H.Y. (2020). DA-CapsUNet: A Dual-Attention Capsule U-Net for Road Extraction from Remote Sensing Imagery. Remote Sens., 12.","DOI":"10.3390\/rs12182866"},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Ren, Y., Li, X., Yang, X., and Xu, H. (2021). Development of a Dual-Attention U-Net Model for Sea Ice and Open Water Classification on SAR Images. IEEE Geosci. Remote Sens. Lett.","DOI":"10.1109\/LGRS.2021.3058049"},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"He, N., Fang, L., and Plaza, A. (2020). Hybrid first and second order attention Unet for building segmentation in remote sensing images. Sci. China Inf. Sci., 63.","DOI":"10.1007\/s11432-019-2791-7"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Pan, X., Yang, F., Gao, L., Chen, Z., Zhang, B., Fan, H., and Ren, J. (2019). Building extraction from high-resolution aerial imagery using a generative adversarial network with spatial and channel attention mechanisms. Remote Sens., 11.","DOI":"10.3390\/rs11080917"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"156349","DOI":"10.1109\/ACCESS.2019.2947286","article-title":"Remote Sensing Image Change Detection Based on Information Transmission and Attention Mechanism","volume":"7","author":"Liu","year":"2019","journal-title":"IEEE Access"},{"key":"ref_77","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_78","doi-asserted-by":"crossref","first-page":"134677","DOI":"10.1109\/ACCESS.2019.2939152","article-title":"Temporal Attention Networks for Multitemporal Multisensor Crop Classification","volume":"7","author":"Li","year":"2019","journal-title":"IEEE Access"},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Mei, X.G., Pan, E.T., Ma, Y., Dai, X.B., Huang, J., Fan, F., Du, Q.L., Zheng, H., and Ma, J.Y. (2019). Spectral-Spatial Attention Networks for Hyperspectral Image Classification. Remote Sens., 11.","DOI":"10.3390\/rs11080963"},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Ma, F., Gao, F., Sun, J.P., Zhou, H.Y., and Hussain, A. (2019). Attention Graph Convolution Network for Image Segmentation in Big SAR Imagery Data. Remote Sens., 11.","DOI":"10.3390\/rs11212586"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"1447","DOI":"10.1109\/JSTARS.2020.3044060","article-title":"Research on Change Detection Method of High-Resolution Remote Sensing Images Based on Subpixel Convolution","volume":"14","author":"Luo","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Li, Y., Chen, R., Zhang, Y., Zhang, M., and Chen, L. (2020). Multi-label remote sensing image scene classification by combining a convolutional neural network and a graph neural network. Remote Sens., 12.","DOI":"10.3390\/rs12234003"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.infsof.2008.09.009","article-title":"Systematic literature reviews in software engineering\u2014A systematic literature review","volume":"51","author":"Kitchenham","year":"2009","journal-title":"Inf. Softw. Technol."},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Chen, L.F., Zhang, P., Xing, J., Li, Z.H., Xing, X.M., and Yuan, Z.H. (2020). A Multi-Scale Deep Neural Network for Water Detection from SAR Images in the Mountainous Areas. Remote Sens., 12.","DOI":"10.3390\/rs12193205"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1080\/2150704X.2020.1868602","article-title":"A method of water change monitoring in remote image time series based on long short time memory","volume":"12","author":"Yang","year":"2021","journal-title":"Remote Sens. Lett."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"111945","DOI":"10.1016\/j.rse.2020.111945","article-title":"Recurrent Shadow Attention Model (RSAM) for shadow removal in high-resolution urban land-cover mapping","volume":"247","author":"Zhang","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/j.isprsjprs.2017.06.001","article-title":"A review of supervised object-based land-cover image classification","volume":"130","author":"Ma","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"1865","DOI":"10.1109\/JPROC.2017.2675998","article-title":"Remote Sensing Image Scene Classification: Benchmark and State of the Art","volume":"105","author":"Cheng","year":"2017","journal-title":"Proc. IEEE"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"389","DOI":"10.5721\/EuJRS20144723","article-title":"A Review of Remote Sensing Image Classification Techniques: The Role of Spatio-contextual Information","volume":"47","author":"Li","year":"2014","journal-title":"Eur. J. Remote Sens."},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Alem, A., and Kumar, S. (2020, January 4\u20135). Deep Learning Methods for Land Cover and Land Use Classification in Remote Sensing: A Review. Proceedings of the 2020 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), Noida, India.","DOI":"10.1109\/ICRITO48877.2020.9197824"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"1057","DOI":"10.1109\/LGRS.2019.2938555","article-title":"FRF-Net: Land Cover Classification from Large-Scale VHR Optical Remote Sensing Images","volume":"17","author":"Sang","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"179547","DOI":"10.1109\/ACCESS.2020.3024133","article-title":"Weakly Supervised Learning for Land Cover Mapping of Satellite Image Time Series via Attention-Based CNN","volume":"8","author":"Ienco","year":"2020","journal-title":"IEEE Access"},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"2430","DOI":"10.1109\/TGRS.2020.3005431","article-title":"Hyperspectral Image Classification Based on 3-D Octave Convolution with Spatial-Spectral Attention Network","volume":"59","author":"Tang","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Feng, Q.L., Yang, J.Y., Liu, Y.M., Ou, C., Zhu, D.H., Niu, B.W., Liu, J.T., and Li, B.G. (2020). Multi-Temporal Unmanned Aerial Vehicle Remote Sensing for Vegetable Mapping Using an Attention-Based Recurrent Convolutional Neural Network. Remote Sens., 12.","DOI":"10.3390\/rs12101668"},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"Li, Y.Y., Huang, Q., Pei, X., Jiao, L.C., and Shang, R.H. (2020). RADet: Refine Feature Pyramid Network and Multi-Layer Attention Network for Arbitrary-Oriented Object Detection of Remote Sensing Images. Remote Sens., 12.","DOI":"10.3390\/rs12030389"},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"Zhou, D., Wang, G., He, G., Long, T., Yin, R., Zhang, Z., Chen, S., and Luo, B. (2020). Robust building extraction for high spatial resolution remote sensing images with self-attention network. Sensors, 20.","DOI":"10.3390\/s20247241"},{"key":"ref_97","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. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"1331","DOI":"10.1109\/TGRS.2020.3005151","article-title":"An Anchor-Free Method Based on Feature Balancing and Refinement Network for Multiscale Ship Detection in SAR Images","volume":"59","author":"Fu","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"1337","DOI":"10.1007\/s10346-020-01353-2","article-title":"Landslide detection from an open satellite imagery and digital elevation model dataset using attention boosted convolutional neural networks","volume":"17","author":"Ji","year":"2020","journal-title":"Landslides"},{"key":"ref_100","doi-asserted-by":"crossref","unstructured":"Yao, Z., Jia, J., and Qian, Y. (2021). Mcnet: Multi-scale feature extraction and content-aware reassembly cloud detection model for remote sensing images. Symmetry, 13.","DOI":"10.3390\/sym13010028"},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"173627","DOI":"10.1109\/ACCESS.2020.3024546","article-title":"Geospatial Contextual Attention Mechanism for Automatic and Fast Airport Detection in SAR Imagery","volume":"8","author":"Tan","year":"2020","journal-title":"IEEE Access"},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.isprsjprs.2020.07.002","article-title":"Cross-regional oil palm tree counting and detection via a multi-level attention domain adaptation network","volume":"167","author":"Zheng","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"146627","DOI":"10.1109\/ACCESS.2020.3015587","article-title":"Deep Attention and Multi-Scale Networks for Accurate Remote Sensing Image Segmentation","volume":"8","author":"Qi","year":"2020","journal-title":"IEEE Access"},{"key":"ref_104","unstructured":"Xiao, D., Wang, Z., Wu, Y., Gao, X., and Sun, X. (2020). Terrain Segmentation in Polarimetric SAR Images Using Dual-Attention Fusion Network. IEEE Geosci. Remote Sens. Lett."},{"key":"ref_105","doi-asserted-by":"crossref","unstructured":"Li, J.L., Xiu, J.P., Yang, Z.Q., and Liu, C. (2020). Dual Path Attention Net for Remote Sensing Semantic Image Segmentation. Isprs Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9100571"},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"1618","DOI":"10.1109\/TGRS.2020.2994253","article-title":"Remote Sensing Image Super-Resolution Using Novel Dense-Sampling Networks","volume":"59","author":"Dong","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"166304","DOI":"10.1109\/ACCESS.2020.3022882","article-title":"Non-Locally up-Down Convolutional Attention Network for Remote Sensing Image Super-Resolution","volume":"8","author":"Wang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"27163","DOI":"10.1109\/ACCESS.2020.2971502","article-title":"A Remote-Sensing Image Pan-Sharpening Method Based on Multi-Scale Channel Attention Residual Network","volume":"8","author":"Li","year":"2020","journal-title":"IEEE Access"},{"key":"ref_109","doi-asserted-by":"crossref","unstructured":"Li, J.J., Cui, R.X., Li, B., Song, R., Li, Y.S., and Du, Q. (2019). Hyperspectral Image Super-Resolution with 1D-2D Attentional Convolutional Neural Network. Remote Sens., 11.","DOI":"10.3390\/rs11232859"},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"6430627","DOI":"10.1155\/2020\/6430627","article-title":"Change Detection of Remote Sensing Images Based on Attention Mechanism","volume":"2020","author":"Chen","year":"2020","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"1194","DOI":"10.1109\/JSTARS.2020.3037893","article-title":"DASNet: Dual Attentive Fully Convolutional Siamese Networks for Change Detection in High-Resolution Satellite Images","volume":"14","author":"Chen","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_112","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Zhang, S.Z., Li, Y., and Zhang, Y.N. (2020). Coarse-to-Fine Satellite Images Change Detection Framework via Boundary-Aware Attentive Network. Sensors, 20.","DOI":"10.3390\/s20236735"},{"key":"ref_113","doi-asserted-by":"crossref","unstructured":"Gu, Z.Q., Zhan, Z.Q., Yuan, Q.Q., and Yan, L. (2019). Single Remote Sensing Image Dehazing Using a Prior-Based Dense Attentive Network. Remote Sens., 11.","DOI":"10.3390\/rs11243008"},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"1645","DOI":"10.1109\/LGRS.2019.2902222","article-title":"Void Filling of Digital Elevation Models with Deep Generative Models","volume":"16","author":"Gavriil","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1109\/TGRS.2020.2993319","article-title":"SAR Image Despeckling Employing a Recursive Deep CNN Prior","volume":"59","author":"Shen","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_116","doi-asserted-by":"crossref","unstructured":"Li, J., Lin, D.Y., Wang, Y., Xu, G.L., Zhang, Y.Y., Ding, C.B., and Zhou, Y.H. (2020). Deep Discriminative Representation Learning with Attention Map for Scene Classification. Remote Sens., 12.","DOI":"10.3390\/rs12091366"},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"1087","DOI":"10.1109\/LGRS.2019.2937872","article-title":"Remote Sensing Image Classification via Improved Cross-Entropy Loss and Transfer Learning Strategy Based on Deep Convolutional Neural Networks","volume":"17","author":"Bahri","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"4748","DOI":"10.1109\/JSTARS.2020.3017544","article-title":"Global prototypical network for few-shot hyperspectral image classification","volume":"13","author":"Zhang","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"9565","DOI":"10.1080\/01431161.2020.1800129","article-title":"Inception residual attention network for remote sensing image super-resolution","volume":"41","author":"Lei","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_120","doi-asserted-by":"crossref","unstructured":"Cheng, W.S., Yang, W., Wang, M., Wang, G., and Chen, J.Y. (2019). Context Aggregation Network for Semantic Labeling in Aerial Images. Remote Sens., 11.","DOI":"10.3390\/rs11101158"},{"key":"ref_121","doi-asserted-by":"crossref","unstructured":"Gbodjo, Y.J.E., Ienco, D., Leroux, L., Interdonato, R., Gaetano, R., and Ndao, B. (2020). Object-based multi-temporal and multi-source land cover mapping leveraging hierarchical class relationships. Remote Sens., 12.","DOI":"10.3390\/rs12172814"},{"key":"ref_122","doi-asserted-by":"crossref","unstructured":"Liang, L., and Wang, G. (2021). Efficient recurrent attention network for remote sensing scene classification. IET Image Process.","DOI":"10.1049\/ipr2.12139"},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"71353","DOI":"10.1109\/ACCESS.2020.2986267","article-title":"Small Sample Classification of Hyperspectral Remote Sensing Images Based on Sequential Joint Deeping Learning Model","volume":"8","author":"Wang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"1281","DOI":"10.1109\/TGRS.2019.2945591","article-title":"Pixel-Level Remote Sensing Image Recognition Based on Bidirectional Word Vectors","volume":"58","author":"You","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_125","doi-asserted-by":"crossref","unstructured":"Zhang, X.K., Pun, M.O., and Liu, M. (2021). Semi-Supervised Multi-Temporal Deep Representation Fusion Network for Landslide Mapping from Aerial Orthophotos. Remote Sens., 13.","DOI":"10.3390\/rs13040548"},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"4369","DOI":"10.1109\/JSTARS.2020.3012443","article-title":"HSI-IPNet: Hyperspectral Imagery Inpainting by Deep Learning With Adaptive Spectral Extraction","volume":"13","author":"Wong","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_127","doi-asserted-by":"crossref","unstructured":"Xu, R.D., Tao, Y.T., Lu, Z.Y., and Zhong, Y.F. (2018). Attention-Mechanism-Containing Neural Networks for High-Resolution Remote Sensing Image Classification. Remote Sens., 10.","DOI":"10.3390\/rs10101602"},{"key":"ref_128","doi-asserted-by":"crossref","first-page":"142483","DOI":"10.1109\/ACCESS.2020.3013898","article-title":"Semantic Segmentation of Marine Remote Sensing Based on a Cross Direction Attention Mechanism","volume":"8","author":"Gao","year":"2020","journal-title":"IEEE Access"},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"522","DOI":"10.1109\/TGRS.2020.2995575","article-title":"Hyperspectral Image Classification Using Mixed Convolutions and Covariance Pooling","volume":"59","author":"Zheng","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_130","doi-asserted-by":"crossref","unstructured":"Zhao, L., Yi, J., Li, X., Hu, W., Wu, J., and Zhang, G. (2021). Compact Band Weighting Module Based on Attention-Driven for Hyperspectral Image Classification. IEEE Trans. Geosci. Remote Sens.","DOI":"10.1109\/TGRS.2021.3053397"},{"key":"ref_131","doi-asserted-by":"crossref","first-page":"3246","DOI":"10.1109\/TGRS.2019.2951445","article-title":"Heterogeneous Transfer Learning for Hyperspectral Image Classification Based on Convolutional Neural Network","volume":"58","author":"He","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"856","DOI":"10.1049\/ipr2.12067","article-title":"Attentive generative adversarial network for removing thin cloud from a single remote sensing image","volume":"15","author":"Chen","year":"2021","journal-title":"IET Image Process."},{"key":"ref_133","doi-asserted-by":"crossref","unstructured":"Wang, J., Xiao, H., Chen, L., Xing, J., Pan, Z., Luo, R., and Cai, X. (2021). Integrating weighted feature fusion and the spatial attention module with convolutional neural networks for automatic aircraft detection from sar images. Remote Sens., 13.","DOI":"10.3390\/rs13050910"},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1016\/j.isprsjprs.2020.08.010","article-title":"NDVI-Net: A fusion network for generating high-resolution normalized difference vegetation index in remote sensing","volume":"168","author":"Zhang","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"9277","DOI":"10.1109\/TGRS.2019.2924818","article-title":"Remote Sensing Image Superresolution Using Deep Residual Channel Attention","volume":"57","author":"Haut","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_136","unstructured":"Dong, X., Xi, Z., Sun, X., and Yang, L. (October, January 26). Remote Sensing Image Super-Resolution via Enhanced Back-Projection Networks. Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS), Waikoloa, HI, USA."},{"key":"ref_137","doi-asserted-by":"crossref","first-page":"4789","DOI":"10.1109\/JSTARS.2020.3016739","article-title":"Deep Collaborative Attention Network for Hyperspectral Image Classification by Combining 2-D CNN and 3-D CNN","volume":"13","author":"Guo","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_138","doi-asserted-by":"crossref","unstructured":"Li, R., Zheng, S.Y., Duan, C.X., Yang, Y., and Wang, X.Q. (2020). Classification of Hyperspectral Image Based on Double-Branch Dual-Attention Mechanism Network. Remote Sens., 12.","DOI":"10.20944\/preprints201912.0059.v2"},{"key":"ref_139","doi-asserted-by":"crossref","unstructured":"Wang, J., Huang, R., Guo, S., Li, L., Zhu, M., Yang, S., and Jiao, L. (2021). NAS-Guided Lightweight Multiscale Attention Fusion Network for Hyperspectral Image Classification. IEEE Trans. Geosci. Remote Sens.","DOI":"10.1109\/TGRS.2021.3049377"},{"key":"ref_140","doi-asserted-by":"crossref","first-page":"1267","DOI":"10.1109\/JSTARS.2020.3041783","article-title":"Learning Slimming SAR Ship Object Detector through Network Pruning and Knowledge Distillation","volume":"14","author":"Chen","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_141","unstructured":"Li, R., Wang, X., Wang, J., Song, Y., and Lei, L. (2020). SAR Target Recognition Based on Efficient Fully Convolutional Attention Block CNN. IEEE Geosci. Remote Sens. Lett."},{"key":"ref_142","doi-asserted-by":"crossref","unstructured":"Qin, J., Wang, B., Wu, Y.L., Lu, Q., and Zhu, H.C. (2021). Identifying Pine Wood Nematode Disease Using UAV Images and Deep Learning Algorithms. Remote Sens., 13.","DOI":"10.3390\/rs13020162"},{"key":"ref_143","first-page":"49","article-title":"Enhanced change detection index for disaster response, recovery assessment and monitoring of accessibility and open spaces (camp sites)","volume":"57","author":"So","year":"2017","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_144","doi-asserted-by":"crossref","unstructured":"Ghaffarian, S., Kerle, N., Pasolli, E., and Jokar Arsanjani, J. (2019). Post-Disaster Building Database Updating Using Automated Deep Learning: An Integration of Pre-Disaster OpenStreetMap and Multi-Temporal Satellite Data. Remote Sens., 11.","DOI":"10.3390\/rs11202427"},{"key":"ref_145","doi-asserted-by":"crossref","unstructured":"Kumar, S., Anouncia, M., Johnson, S., Agarwal, A., and Dwivedi, P. (2012, January 21\u201322). Agriculture change detection model using remote sensing images and GIS: Study area Vellore. Proceedings of the 2012 International Conference on Radar, Communication and Computing (ICRCC), Tiruvannamalai, India.","DOI":"10.1109\/ICRCC.2012.6450547"},{"key":"ref_146","doi-asserted-by":"crossref","unstructured":"Liu, Q., Zhou, H., Xu, Q., Liu, X., and Wang, Y. (2020). PSGAN: A Generative Adversarial Network for Remote Sensing Image Pan-Sharpening. IEEE Trans. Geosci. Remote Sens.","DOI":"10.1109\/TGRS.2020.3042974"},{"key":"ref_147","unstructured":"(2021, March 17). Web of Science. Available online: www.isiwebofknowledge.com."},{"key":"ref_148","doi-asserted-by":"crossref","unstructured":"Choi, Y., Uh, Y., Yoo, J., and Ha, J.W. (2020, January 14\u201319). StarGAN v2: Diverse Image Synthesis for Multiple Domains. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00821"},{"key":"ref_149","unstructured":"Zhang, H., Goodfellow, I., Metaxas, D., and Odena, A. (2019, January 9\u201315). Self-Attention Generative Adversarial Networks. Proceedings of the 36th International Conference on Machine Learning, Long Beach, CA, USA."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/15\/2965\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:36:03Z","timestamp":1760164563000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/15\/2965"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,28]]},"references-count":149,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2021,8]]}},"alternative-id":["rs13152965"],"URL":"https:\/\/doi.org\/10.3390\/rs13152965","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7,28]]}}}