{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T23:43:18Z","timestamp":1782517398529,"version":"3.54.5"},"reference-count":36,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,3,18]],"date-time":"2023-03-18T00:00:00Z","timestamp":1679097600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Project of Dynamic Remote Sensing Monitoring of Bare Soil in Daxing District, Beijing, China","award":["DXCG_21_0904"],"award-info":[{"award-number":["DXCG_21_0904"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Accurate monitoring of bare soil land (BSL) is an urgent need for environmental governance and optimal utilization of land resources. High-resolution imagery contains rich semantic information, which is beneficial for the recognition of objects on the ground. Simultaneously, it is susceptible to the impact of its background. We propose a semantic segmentation model, Deeplabv3+-M-CBAM, for extracting BSL. First, we replaced the Xception of Deeplabv3+ with MobileNetV2 as the backbone network to reduce the number of parameters. Second, to distinguish BSL from the background, we employed the convolutional block attention module (CBAM) via a combination of channel attention and spatial attention. For model training, we built a BSL dataset based on BJ-2 satellite images. The test result for the F1 of the model was 88.42%. Compared with Deeplabv3+, the classification accuracy improved by 8.52%, and the segmentation speed was 2.34 times faster. In addition, compared with the visual interpretation, the extraction speed improved by 11.5 times. In order to verify the transferable performance of the model, Jilin-1GXA images were used for the transfer test, and the extraction accuracies for F1, IoU, recall and precision were 86.07%, 87.88%, 87.00% and 95.80%, respectively. All of these experiments show that Deeplabv3+-M-CBAM achieved efficient and accurate extraction results and a well transferable performance for BSL. The methodology proposed in this study exhibits its application value for the refinement of environmental governance and the surveillance of land use.<\/jats:p>","DOI":"10.3390\/rs15061646","type":"journal-article","created":{"date-parts":[[2023,3,20]],"date-time":"2023-03-20T03:09:37Z","timestamp":1679281777000},"page":"1646","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Automatic Extraction of Bare Soil Land from High-Resolution Remote Sensing Images Based on Semantic Segmentation with Deep Learning"],"prefix":"10.3390","volume":"15","author":[{"given":"Chen","family":"He","sequence":"first","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yalan","family":"Liu","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dacheng","family":"Wang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shufu","family":"Liu","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Linjun","family":"Yu","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuhuan","family":"Ren","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,18]]},"reference":[{"key":"ref_1","first-page":"489","article-title":"Dynamics of Bare Soil in A Typical Reddish Soil Loss Region of Southern China: Changting County, Fujian Province","volume":"33","author":"Xu","year":"2013","journal-title":"Sci. Geogr. Sin."},{"key":"ref_2","unstructured":"Anderson, J.R., Hardy, E.E., Roach, J.T., and Witmer, R.E. (1976). Professional Paper, USGS Publications Warehouse."},{"key":"ref_3","unstructured":"Gregorio, A.D., and Jansen, L.J.M. (2000). Food and Agriculture Organization of the United Nations. Land Cover Classification System: LCCS: Classification Concepts and User Manual, Food and Agriculture Organization of the United Nations."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2485","DOI":"10.1080\/01431160110115582","article-title":"Land-Cover Classification of China: Integrated Analysis of AVHRR Imagery and Geophysical Data","volume":"24","author":"Liu","year":"2003","journal-title":"Int. J. Remote Sens."},{"key":"ref_5","first-page":"994","article-title":"Explanation of Current Land Use Condition Classification for National Standard of the People\u2019s Republic of China","volume":"22","author":"Chen","year":"2007","journal-title":"J. Nat. Resour."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1080\/17538941003777521","article-title":"Production of Global Land Cover Data\u2014GLCNMO","volume":"4","author":"Tateishi","year":"2011","journal-title":"Int. J. Digital Earth"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1016\/S0034-4257(02)00078-0","article-title":"Global Land Cover Mapping from MODIS: Algorithms and Early Results","volume":"83","author":"Friedl","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Nguyen, C.T., Chidthaisong, A., Kieu Diem, P., and Huo, L.-Z. (2021). A Modified Bare Soil Index to Identify Bare Land Features during Agricultural Fallow-Period in Southeast Asia Using Landsat 8. Land, 10.","DOI":"10.3390\/land10030231"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1080\/10095020.2020.1838957","article-title":"China\u2019s High-Resolution Optical Remote Sensing Satellites and Their Mapping Applications","volume":"24","author":"Li","year":"2021","journal-title":"Geo-Spat. Inf. Sci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.isprsjprs.2022.02.022","article-title":"Extracting Planar Roof Structures from Very High Resolution Images Using Graph Neural Networks","volume":"187","author":"Zhao","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Ghandorh, H., Boulila, W., Masood, S., Koubaa, A., Ahmed, F., and Ahmad, J. (2022). Semantic Segmentation and Edge Detection\u2014Approach to Road Detection in Very High Resolution Satellite Images. Remote Sens., 14.","DOI":"10.3390\/rs14030613"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3120","DOI":"10.1109\/JSTARS.2021.3060769","article-title":"A Deep Learning Method of Water Body Extraction From High Resolution Remote Sensing Images With Multisensors","volume":"14","author":"Li","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Karra, K., Kontgis, C., Statman-Weil, Z., Mazzariello, J.C., Mathis, M., and Brumby, S.P. (2021, January 11\u201316). Global Land Use\/Land Cover with Sentinel 2 and Deep Learning. Proceedings of the 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium.","DOI":"10.1109\/IGARSS47720.2021.9553499"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1109\/JPROC.2020.3004555","article-title":"A Comprehensive Survey on Transfer Learning","volume":"109","author":"Zhuang","year":"2021","journal-title":"Proc. IEEE"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Toldo, M., Michieli, U., and Zanuttigh, P. (2021, January 3\u20138). Unsupervised Domain Adaptation in Semantic Segmentation via Orthogonal and Clustered Embeddings. Proceedings of the 2021 IEEE Winter Conference on Applications of Computer Vision (WACV), Waikoloa, HI, USA.","DOI":"10.1109\/WACV48630.2021.00140"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Guizilini, V., Li, J., Ambru\u015f, R., and Gaidon, A. (2021, January 10\u201317). Geometric Unsupervised Domain Adaptation for Semantic Segmentation. Proceedings of the 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00842"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Marsden, R.A., Wiewel, F., D\u00f6bler, M., Yang, Y., and Yang, B. (2022, January 18\u201323). Continual Unsupervised Domain Adaptation for Semantic Segmentation Using a Class-Specific Transfer. Proceedings of the 2022 International Joint Conference on Neural Networks (IJCNN), Padua, Italy.","DOI":"10.1109\/IJCNN55064.2022.9892200"},{"key":"ref_18","first-page":"2593","article-title":"Unsupervised Model Adaptation for Continual Semantic Segmentation","volume":"35","author":"Stan","year":"2021","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"ref_19","unstructured":"Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., and Yuille, A.L. (2014). Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs. arXiv."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs","volume":"40","author":"Chen","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_21","unstructured":"Chen, L.-C., Papandreou, G., Schroff, F., and Adam, H. (2017). Rethinking Atrous Convolution for Semantic Image Segmentation. arXiv."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H. (2018). Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation. arXiv.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Lin, Y., Xu, D., Wang, N., Shi, Z., and Chen, Q. (2021). Road Extraction from Very-High-Resolution Remote Sensing Images via a Nested SE-Deeplab Model. Remote Sens., 13.","DOI":"10.3390\/rs13040783"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Ren, Y., Zhang, X., Ma, Y., Yang, Q., Wang, C., Liu, H., and Qi, Q. (2020). Full Convolutional Neural Network Based on Multi-Scale Feature Fusion for the Class Imbalance Remote Sensing Image Classification. Remote Sens., 12.","DOI":"10.3390\/rs12213547"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"106265","DOI":"10.1016\/j.catena.2022.106265","article-title":"NDBSI: A Normalized Difference Bare Soil Index for Remote Sensing to Improve Bare Soil Mapping Accuracy in Urban and Rural Areas","volume":"214","author":"Liu","year":"2022","journal-title":"CATENA"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1007\/s11263-014-0733-5","article-title":"The Pascal Visual Object Classes Challenge: A Retrospective","volume":"111","author":"Everingham","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.-C. (2018, January 18\u201323). MobileNetV2: Inverted Residuals and Linear Bottlenecks. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Ferrari, V., Hebert, M., Sminchisescu, C., and Weiss, Y. CBAM: Convolutional Block Attention Module. Proceedings of the Computer Vision\u2014ECCV 2018.","DOI":"10.1007\/978-3-030-01249-6"},{"key":"ref_29","unstructured":"Yosinski, J., Clune, J., Bengio, Y., and Lipson, H. (2014, January 8). How Transferable Are Features in Deep Neural Networks?. Proceedings of the 27th International Conference on Neural Information Processing Systems\u2014Volume 2, Montreal, QC, Canada."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"818","DOI":"10.1007\/978-3-319-10590-1_53","article-title":"Visualizing and Understanding Convolutional Networks","volume":"Volume 8689","author":"Fleet","year":"2014","journal-title":"Computer Vision\u2014ECCV 2014"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature Pyramid Networks for Object Detection. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_32","first-page":"234","article-title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","volume":"Volume 9351","author":"Navab","year":"2015","journal-title":"Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI 2015"},{"key":"ref_33","unstructured":"Woo, S., Kim, D., Cho, D., and Kweon, I.S. (2018, January 3). LinkNet: Relational Embedding for Scene Graph. Proceedings of the 32nd International Conference on Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1016\/j.isprsjprs.2021.08.024","article-title":"National-Scale Greenhouse Mapping for High Spatial Resolution Remote Sensing Imagery Using a Dense Object Dual-Task Deep Learning Framework: A Case Study of China","volume":"181","author":"Ma","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.isprsjprs.2021.05.019","article-title":"Rapid and Large-Scale Mapping of Flood Inundation via Integrating Spaceborne Synthetic Aperture Radar Imagery with Unsupervised Deep Learning","volume":"178","author":"Jiang","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_36","first-page":"102686","article-title":"Ultrahigh-Resolution Boreal Forest Canopy Mapping: Combining UAV Imagery and Photogrammetric Point Clouds in a Deep-Learning-Based Approach","volume":"107","author":"Li","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/6\/1646\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:58:22Z","timestamp":1760122702000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/6\/1646"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,18]]},"references-count":36,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["rs15061646"],"URL":"https:\/\/doi.org\/10.3390\/rs15061646","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,18]]}}}