{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T12:46:17Z","timestamp":1773060377790,"version":"3.50.1"},"reference-count":50,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2021,9,29]],"date-time":"2021-09-29T00:00:00Z","timestamp":1632873600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100005089","name":"Beijing Municipal Natural Science Foundation","doi-asserted-by":"publisher","award":["L191020"],"award-info":[{"award-number":["L191020"]}],"id":[{"id":"10.13039\/501100005089","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Remote sensing has now been widely used in various fields, and the research on the automatic land-cover segmentation methods of remote sensing imagery is significant to the development of remote sensing technology. Deep learning methods, which are developing rapidly in the field of semantic segmentation, have been widely applied to remote sensing imagery segmentation. In this work, a novel deep learning network\u2014Dual Encoder with Attention Network (DEANet) is proposed. In this network, a dual-branch encoder structure, whose first branch is used to generate a rough guidance feature map as area attention to help re-encode feature maps in the next branch, is proposed to improve the encoding ability of the network, and an improved pyramid partial decoder (PPD) based on the parallel partial decoder is put forward to make fuller use of the features form the encoder along with the receptive filed block (RFB). In addition, an edge attention module using the transfer learning method is introduced to explicitly advance the segmentation performance in edge areas. Except for structure, a loss function composed with the weighted Cross Entropy (CE) loss and weighted Union subtract Intersection (UsI) loss is designed for training, where UsI loss represents a new region-based aware loss which replaces the IoU loss to adapt to multi-classification tasks. Furthermore, a detailed training strategy for the network is introduced as well. Extensive experiments on three public datasets verify the effectiveness of each proposed module in our framework and demonstrate that our method achieves more excellent performance over some state-of-the-art methods.<\/jats:p>","DOI":"10.3390\/rs13193900","type":"journal-article","created":{"date-parts":[[2021,10,8]],"date-time":"2021-10-08T21:26:20Z","timestamp":1633728380000},"page":"3900","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["DEANet: Dual Encoder with Attention Network for Semantic Segmentation of Remote Sensing Imagery"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2447-4676","authenticated-orcid":false,"given":"Haoran","family":"Wei","sequence":"first","affiliation":[{"name":"State Key Laboratory of Intelligent Control and Decision of Complex Systems, Beijing Institute of Technology, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangyang","family":"Xu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Intelligent Control and Decision of Complex Systems, Beijing Institute of Technology, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ni","family":"Ou","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Intelligent Control and Decision of Complex Systems, Beijing Institute of Technology, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7605-2913","authenticated-orcid":false,"given":"Xinru","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8795-5333","authenticated-orcid":false,"given":"Yaping","family":"Dai","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Intelligent Control and Decision of Complex Systems, Beijing Institute of Technology, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.5589\/m02-075","article-title":"A rule-based urban land use inferring method for fine-resolution multispectral imagery","volume":"29","author":"Zhang","year":"2003","journal-title":"Can. J. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Valentijn, T., Margutti, J., van den Homberg, M., and Laaksonen, J. (2020). Multi-Hazard and Spatial Transferability of a CNN for Automated Building Damage Assessment. Remote Sens., 12.","DOI":"10.3390\/rs12172839"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Gul\u00e1csi, A., and Kov\u00e1cs, F. (2020). Sentinel-1-Imagery-Based High-Resolution Water Cover Detection on Wetlands, Aided by Google Earth Engine. Remote Sens., 12.","DOI":"10.3390\/rs12101614"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1158","DOI":"10.1080\/10106049.2015.1120354","article-title":"Soil erosion prediction based on land cover dynamics at the Semenyih watershed in Malaysia using LTM and USLE models","volume":"31","author":"Rizeei","year":"2016","journal-title":"Geocarto Int."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1332","DOI":"10.1080\/10106049.2018.1489420","article-title":"Assessment and planning for integrated river basin management using remote sensing, SWAT model and morphometric analysis (case study: Kaddam river basin, India)","volume":"34","author":"Parupalli","year":"2019","journal-title":"Geocarto Int."},{"key":"ref_6","first-page":"11","article-title":"Monitoring and mapping rural urbanization and land use changes using Landsat data in the northeast subtropical region of Vietnam","volume":"23","author":"Ha","year":"2020","journal-title":"Egypt. J. Remote Sens. Space Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.compag.2017.07.003","article-title":"Agricultural plastic waste spatial estimation by Landsat 8 satellite images","volume":"141","author":"Lanorte","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Xia, L., Zhang, X., Zhang, J., Yang, H., and Chen, T. (2021). Building Extraction from Very-High-Resolution Remote Sensing Images Using Semi-Supervised Semantic Edge Detection. Remote Sens., 13.","DOI":"10.3390\/rs13112187"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"111017","DOI":"10.1016\/j.rse.2018.12.016","article-title":"Characterizing land cover\/land use from multiple years of Landsat and MODIS time series: A novel approach using land surface phenology modeling and random forest classifier","volume":"238","author":"Nguyen","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/j.rse.2011.11.020","article-title":"A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery","volume":"118","author":"Duro","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Ichim, L., and Popescu, D. (2020). Segmentation of Vegetation and Flood from Aerial Images Based on Decision Fusion of Neural Networks. Remote Sens., 12.","DOI":"10.3390\/rs12152490"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Schlosser, A.D., Szab\u00f3, G., Bertalan, L., Varga, Z., Enyedi, P., and Szab\u00f3, S. (2020). Building Extraction Using Orthophotos and Dense Point Cloud Derived from Visual Band Aerial Imagery Based on Machine Learning and Segmentation. Remote Sens., 12.","DOI":"10.3390\/rs12152397"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Ayhan, B., Kwan, C., Budavari, B., Kwan, L., Lu, Y., Perez, D., Li, J., Skarlatos, D., and Vlachos, M. (2020). Vegetation Detection Using Deep Learning and Conventional Methods. Remote Sens., 12.","DOI":"10.3390\/rs12152502"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Song, A., Kim, Y., and Han, Y. (2020). Uncertainty Analysis for Object-Based Change Detection in Very High-Resolution Satellite Images Using Deep Learning Network. Remote Sens., 12.","DOI":"10.3390\/rs12152345"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Tran, A.T., Nguyen, K.A., Liou, Y.A., Le, M.H., Vu, V.T., and Nguyen, D.D. (2021). Classification and Observed Seasonal Phenology of Broadleaf Deciduous Forests in a Tropical Region by Using Multitemporal Sentinel-1A and Landsat 8 Data. Forests, 12.","DOI":"10.3390\/f12020235"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"473","DOI":"10.1016\/j.isprsjprs.2011.02.006","article-title":"Unsupervised image segmentation evaluation and refinement using a multi-scale approach","volume":"66","author":"Johnson","year":"2011","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"6554","DOI":"10.1080\/01431161.2017.1362131","article-title":"A central-point-enhanced convolutional neural network for high-resolution remote-sensing image classification","volume":"38","author":"Pan","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1109\/TGRS.2016.2612821","article-title":"Convolutional Neural Networks for Large-Scale Remote-Sensing Image Classification","volume":"55","author":"Maggiori","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2325","DOI":"10.1109\/LGRS.2017.2763738","article-title":"Deep Fully Convolutional Networks for the Detection of Informal Settlements in VHR Images","volume":"14","author":"Persello","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Gibril, M.B., Shafri, H.Z.M., Shanableh, A., Al-Ruzouq, R., Wayayok, A., and Hashim, S.J. (2021). Deep Convolutional Neural Network for Large-Scale Date Palm Tree Mapping from UAV-Based Images. Remote Sens., 13.","DOI":"10.3390\/rs13142787"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2594","DOI":"10.1080\/01431161.2020.1856964","article-title":"DAU-Net: A novel water areas segmentation structure for remote sensing image","volume":"42","author":"Xia","year":"2021","journal-title":"Int. J. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Wang, L., Weng, L., Xia, M., Liu, J., and Lin, H. (2021). Multi-Resolution Supervision Network with an Adaptive Weighted Loss for Desert Segmentation. Remote Sens., 13.","DOI":"10.3390\/rs13112054"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Chen, B., Xia, M., and Huang, J. (2021). Mfanet: A multi-level feature aggregation network for semantic segmentation of land cover. Remote Sens., 13.","DOI":"10.3390\/rs13040731"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2245","DOI":"10.1109\/TGRS.2020.3006872","article-title":"Class-guided feature decoupling network for airborne image segmentation","volume":"59","author":"Zhou","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Chen, J., He, F., Zhang, Y., Sun, G., and Deng, M. (2020). SPMF-Net: Weakly supervised building segmentation by combining superpixel pooling and multi-scale feature fusion. Remote Sens., 12.","DOI":"10.3390\/rs12061049"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"6169","DOI":"10.1109\/TGRS.2020.3026051","article-title":"MAP-Net: Multiple attending path neural network for building footprint extraction from remote sensed imagery","volume":"59","author":"Zhu","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Seong, S., and Choi, J. (2021). Semantic Segmentation of Urban Buildings Using a High-Resolution Network (HRNet) with Channel and Spatial Attention Gates. Remote Sens., 13.","DOI":"10.3390\/rs13163087"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Li, R., Zheng, S., Zhang, C., Duan, C., Su, J., Wang, L., and Atkinson, P.M. (2021). Multiattention Network for Semantic Segmentation of Fine-Resolution Remote Sensing Images. IEEE Trans. Geosci. Remote Sens., 1\u201313.","DOI":"10.1109\/TGRS.2021.3093977"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Niu, R., Sun, X., Tian, Y., Diao, W., Chen, K., and Fu, K. (2021). Hybrid multiple attention network for semantic segmentation in aerial images. IEEE Trans. Geosci. Remote. Sens., 1\u201318.","DOI":"10.1109\/TGRS.2021.3065112"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Liu, S., and Huang, D. (2018, January 8\u201314). Receptive field block net for accurate and fast object detection. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01252-6_24"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Wu, Z., Su, L., and Huang, Q. (2019, January 15\u201320). Cascaded partial decoder for fast and accurate salient object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00403"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2626","DOI":"10.1109\/TMI.2020.2996645","article-title":"Inf-net: Automatic covid-19 lung infection segmentation from ct images","volume":"39","author":"Fan","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"3480","DOI":"10.1109\/JSTARS.2019.2924086","article-title":"Cascaded detection framework based on a novel backbone network and feature fusion","volume":"12","author":"Tian","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Li, Z., Peng, C., Yu, G., Zhang, X., Deng, Y., and Sun, J. (2018). Detnet: A backbone network for object detection. arXiv.","DOI":"10.1007\/978-3-030-01240-3_21"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Liu, Y., Wang, Y., Wang, S., Liang, T., Zhao, Q., Tang, Z., and Ling, H. (2020, January 7\u201312). Cbnet: A novel composite backbone network architecture for object detection. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA.","DOI":"10.1609\/aaai.v34i07.6834"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Fan, D.P., Ji, G.P., Zhou, T., Chen, G., Fu, H., Shen, J., and Shao, L. (2020, January 4\u20138). Pranet: Parallel reverse attention network for polyp segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Lima, Peru.","DOI":"10.1007\/978-3-030-59725-2_26"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Zhao, J.X., Liu, J.J., Fan, D.P., Cao, Y., Yang, J., and Cheng, M.M. (2019, January 27\u201328). EGNet: Edge guidance network for salient object detection. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Korea.","DOI":"10.1109\/ICCV.2019.00887"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Wu, Z., Su, L., and Huang, Q. (2019, January 27\u201328). Stacked cross refinement network for edge-aware salient object detection. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Korea.","DOI":"10.1109\/ICCV.2019.00736"},{"key":"ref_39","unstructured":"Wei, J., Wang, S., and Huang, Q. (2020, January 7\u201312). F3Net: Fusion, Feedback and Focus for Salient Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Boguszewski, A., Batorski, D., Ziemba-Jankowska, N., Dziedzic, T., and Zambrzycka, A. (2021, January 19\u201325). LandCover.ai: Dataset for Automatic Mapping of Buildings, Woodlands, Water and Roads from Aerial Imagery. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, virtual.","DOI":"10.1109\/CVPRW53098.2021.00121"},{"key":"ref_41","unstructured":"Iglovikov, V., Mushinskiy, S., and Osin, V. (2017). Satellite Imagery Feature Detection using Deep Convolutional Neural Network: A Kaggle Competition. arXiv."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Demir, I., Koperski, K., Lindenbaum, D., Pang, G., Huang, J., Basu, S., Hughes, F., Tuia, D., and Raskar, R. (2018, January 18\u201322). Deepglobe 2018: A challenge to parse the earth through satellite images. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00031"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"652","DOI":"10.1109\/TPAMI.2019.2938758","article-title":"Res2net: A new multi-scale backbone architecture","volume":"43","author":"Gao","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H. (2018, January 8\u201314). Encoder-decoder with atrous separable convolution for semantic image segmentation. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_46","unstructured":"Chen, L.C., Papandreou, G., Schroff, F., and Adam, H. (2017). Rethinking atrous convolution for semantic image segmentation. arXiv."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J. (2017, January 21\u201326). Pyramid scene parsing network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Zhao, H., Zhang, Y., Liu, S., Shi, J., Loy, C.C., Lin, D., and Jia, J. (2018, January 8\u201314). Psanet: Point-wise spatial attention network for scene parsing. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01240-3_17"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Cao, Y., Xu, J., Lin, S., Wei, F., and Hu, H. (2019, January 16\u201320). Gcnet: Non-local networks meet squeeze-excitation networks and beyond. Proceedings of the IEEE\/CVF International Conference on Computer Vision Workshops, Long Beach, CA, USA.","DOI":"10.1109\/ICCVW.2019.00246"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Zhang, H., Dana, K., Shi, J., Zhang, Z., Wang, X., Tyagi, A., and Agrawal, A. (2018, January 18\u201322). Context encoding for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00747"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/19\/3900\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:07:20Z","timestamp":1760166440000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/19\/3900"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,29]]},"references-count":50,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2021,10]]}},"alternative-id":["rs13193900"],"URL":"https:\/\/doi.org\/10.3390\/rs13193900","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,29]]}}}