{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T13:58:30Z","timestamp":1770040710028,"version":"3.49.0"},"reference-count":53,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2023,7,25]],"date-time":"2023-07-25T00:00:00Z","timestamp":1690243200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key Science and Technology Project of Inner Mongolia","award":["2021ZD0015"],"award-info":[{"award-number":["2021ZD0015"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This paper focuses on the problems of omission, misclassification, and inter-adhesion due to overly dense distribution, intraclass diversity, and interclass variability when extracting winter wheat (WW) from high-resolution images. This paper proposes a deep supervised network RAunet model with multi-scale features that incorporates a dual-attention mechanism with an improved U-Net backbone network. The model mainly consists of a pyramid input layer, a modified U-Net backbone network, and a side output layer. Firstly, the pyramid input layer is used to fuse the feature information of winter wheat at different scales by constructing multiple input paths. Secondly, the Atrous Spatial Pyramid Pooling (ASPP) residual module and the Convolutional Block Attention Module (CBAM) dual-attention mechanism are added to the U-Net model to form the backbone network of the model, which enhances the feature extraction ability of the model for winter wheat information. Finally, the side output layer consists of multiple classifiers to supervise the results of different scale outputs. Using the RAunet model to extract the spatial distribution information of WW from GF-2 imagery, the experimental results showed that the mIou of the recognition results reached 92.48%, an improvement of 2.66%, 4.15%, 1.42%, 2.35%, 3.76%, and 0.47% compared to FCN, U-Net, DeepLabv3, SegNet, ResUNet, and UNet++, respectively. The superiority of the RAunet model in high-resolution images for WW extraction was verified in effectively improving the accuracy of the spatial distribution information extraction of WW.<\/jats:p>","DOI":"10.3390\/rs15153711","type":"journal-article","created":{"date-parts":[[2023,7,26]],"date-time":"2023-07-26T01:09:01Z","timestamp":1690333741000},"page":"3711","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["A Spatial Distribution Extraction Method for Winter Wheat Based on Improved U-Net"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-7349-7702","authenticated-orcid":false,"given":"Jiahao","family":"Liu","sequence":"first","affiliation":[{"name":"College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China"}]},{"given":"Hong","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China"},{"name":"Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}]},{"given":"Yao","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China"}]},{"given":"Xili","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5831-5329","authenticated-orcid":false,"given":"Tengfei","family":"Qu","sequence":"additional","affiliation":[{"name":"Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}]},{"given":"Haozhe","family":"Tian","sequence":"additional","affiliation":[{"name":"College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China"}]},{"given":"Yuting","family":"Lu","sequence":"additional","affiliation":[{"name":"College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China"}]},{"given":"Jingru","family":"Su","sequence":"additional","affiliation":[{"name":"College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China"}]},{"given":"Dingsheng","family":"Luo","sequence":"additional","affiliation":[{"name":"College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China"}]},{"given":"Yalei","family":"Yang","sequence":"additional","affiliation":[{"name":"Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5611","DOI":"10.3390\/rs70505611","article-title":"Mapping of Agricultural Crops from Single High-Resolution Multispectral Images\u2014Data-Driven Smoothing vs. Parcel-Based Smoothing","volume":"7","author":"Ok","year":"2015","journal-title":"Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.eja.2012.05.009","article-title":"Climatic suitability of the distribution of the winter wheat cultivation zone in China","volume":"43","year":"2012","journal-title":"Eur. J. Agron."},{"key":"ref_3","unstructured":"(2023, July 05). National Bureau of Statistics of China (NBS), Available online: http:\/\/www.stats.gov.cn."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2022.02.016","article-title":"The effects of Landsat image acquisition date on winter wheat classification in the North China Plain","volume":"187","author":"Fan","year":"2022","journal-title":"ISPRS-J. Photogramm. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Zhang, S., Yang, S., Wang, J., Wu, X., Henchiri, M., Javed, T., Zhang, J., and Bai, Y. (2023). Integrating a Novel Irrigation Approximation Method with a Process-Based Remote Sensing Model to Estimate Multi-Years Winter Wheat Yield over the North China Plain. J. Integr. Agric., in press.","DOI":"10.1016\/j.jia.2023.02.036"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/j.rse.2018.12.026","article-title":"Crop type mapping without field-level labels: Random forest transfer and unsupervised clustering techniques","volume":"222","author":"Wang","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1109\/LGRS.2017.2657778","article-title":"Training Deep Convolutional Neural Networks for Land\u2013Cover Classification of High-Resolution Imagery","volume":"14","author":"Scott","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"111322","DOI":"10.1016\/j.rse.2019.111322","article-title":"Land-cover classification with high-resolution remote sensing images using transferable deep models","volume":"237","author":"Tong","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Chen, F., Zhang, W., Song, Y., Liu, L., and Wang, C. (2023). Comparison of Simulated Multispectral Reflectance among Four Sensors in Land Cover Classification. Remote Sens., 15.","DOI":"10.3390\/rs15092373"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Li, W., Zhang, H., Li, W., and Ma, T. (2022). Extraction of Winter Wheat Planting Area Based on Multi-Scale Fusion. Remote Sens., 15.","DOI":"10.3390\/rs15010164"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"113206","DOI":"10.1016\/j.rse.2022.113206","article-title":"A new phenology-based method for mapping wheat and barley using time-series of Sentinel-2 images","volume":"280","author":"Ashourloo","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zakeri, H., Yamazaki, F., and Liu, W. (2017). Texture Analysis and Land Cover Classification of Tehran Using Polarimetric Synthetic Aperture Radar Imagery. Appl. Sci., 7.","DOI":"10.3390\/app7050452"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"106040","DOI":"10.1016\/j.engappai.2023.106040","article-title":"An adaptive position-guided gravitational search algorithm for function optimization and image threshold segmentation","volume":"121","author":"Guo","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"104342","DOI":"10.1016\/j.bspc.2022.104342","article-title":"Gradient-based edge detection with skeletonization (GES) segmentation for magnetic resonance optic nerve images","volume":"80","author":"Feng","year":"2023","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.compmedimag.2015.12.004","article-title":"A coronary artery segmentation method based on multiscale analysis and region growing","volume":"48","author":"Kerkeni","year":"2016","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"3035","DOI":"10.1080\/01431160600617194","article-title":"Parameter selection for region-growing image segmentation algorithms using spatial autocorrelation","volume":"27","author":"Espindola","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2987","DOI":"10.1109\/TGRS.2014.2367129","article-title":"Marker-Controlled Watershed-Based Segmentation of Multiresolution Remote Sensing Images","volume":"53","author":"Gaetano","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_19","first-page":"1337","article-title":"Fully Convolutional Networks for Semantic Segmentation","volume":"79","author":"Long","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-Net: Convolutional Networks for Biomedical Image. 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_21","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1109\/TPAMI.2015.2437384","article-title":"Region-Based Convolutional Networks for Accurate Object Detection and Segmentation","volume":"38","author":"Girshick","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Liu, M., Xie, T., Cheng, X., Deng, J., Yang, M., Wang, X., and Liu, M. (2022). FocusedDropout for Convolutional Neural Network. Appl. Sci., 12.","DOI":"10.3390\/app12157682"},{"key":"ref_23","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_24","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Huang, R., Wang, C., Li, J., and Sui, Y. (2023). DF-UHRNet: A Modified CNN-Based Deep Learning Method for Automatic Sea Ice Classification from Sentinel-1A\/B SAR Images. Remote Sens., 15.","DOI":"10.3390\/rs15092448"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Wu, H., Shi, C., Wang, L., and Jin, Z. (2023). A Cross-Channel Dense Connection and Multi-Scale Dual Aggregated Attention Network for Hyperspectral Image Classification. Remote Sens., 15.","DOI":"10.3390\/rs15092367"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"113485","DOI":"10.1016\/j.rse.2023.113485","article-title":"Surveying coconut trees using high-resolution satellite imagery in remote atolls of the Pacific Ocean","volume":"287","author":"Zheng","year":"2023","journal-title":"Remote Sens. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","volume":"39","author":"Ren","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2237","DOI":"10.1109\/TMM.2019.2900908","article-title":"Salient Object Detection Using Cascaded Convolutional Neural Networks and Adversarial Learning","volume":"21","author":"Tang","year":"2019","journal-title":"IEEE Trans. Multimed."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"280","DOI":"10.1016\/j.isprsjprs.2020.09.025","article-title":"Identifying and mapping individual plants in a highly diverse high-elevation ecosystem using UAV imagery and deep learning","volume":"169","author":"Zhang","year":"2020","journal-title":"ISPRS-J. Photogramm. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.isprsjprs.2019.12.010","article-title":"A convolutional neural network approach for counting and geolocating citrus-trees in UAV multispectral imagery","volume":"160","author":"Osco","year":"2020","journal-title":"ISPRS-J. Photogramm. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_33","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, Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_34","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_35","unstructured":"Chen, L.-C., Papandreou, G., Schroff, F., and Adam, H. (2017). Rethinking Atrous Convolution for Semantic Image Segmentation. arXiv."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.isprsjprs.2020.01.013","article-title":"ResUNet-a: A deep learning framework for semantic segmentation of remotely sensed data","volume":"162","author":"Diakogiannis","year":"2020","journal-title":"ISPRS-J. Photogramm. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"41249","DOI":"10.1007\/s11042-022-13198-z","article-title":"Iris segmentation method based on improved UNet++","volume":"81","author":"Huo","year":"2022","journal-title":"Multimed. Tools Appl."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Sun, H., Wang, B., Wu, Y., and Yang, H. (2023). Deep Learning Method Based on Spectral Characteristic Rein-Forcement for the Extraction of Winter Wheat Planting Area in Complex Agricultural Landscapes. Remote Sens., 15.","DOI":"10.3390\/rs15051301"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Zhou, K., Zhang, Z., Liu, L., Miao, R., Yang, Y., Ren, T., and Yue, M. (2023). Research on SUnet Winter Wheat Identification Method Based on GF-2. Remote Sens., 15.","DOI":"10.3390\/rs15123094"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Tang, Z., Sun, Y., Wan, G., Zhang, K., Shi, H., Zhao, Y., Chen, S., and Zhang, X. (2022). Winter Wheat Lodging Area Extraction Using Deep Learning with GaoFen-2 Satellite Imagery. Remote Sens., 14.","DOI":"10.3390\/rs14194887"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"107146","DOI":"10.1016\/j.compag.2022.107146","article-title":"MTS-CNN: Multi-task semantic segmentation-convolutional neural network for detecting crops and weeds","volume":"199","author":"Kim","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2341","DOI":"10.1016\/j.egypro.2017.12.164","article-title":"Quantitative Analysis of the Water-Energy-Climate Nexus in Shanxi Province, China","volume":"142","author":"Zhu","year":"2017","journal-title":"Energy Procedia"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Abraham, N., and Khan, N.M. (2019, January 8\u201311). A Novel Focal Tversky Loss Function with Improved Attention U-Net for Lesion Segmentation. Proceedings of the IEEE 16th International Symposium on Biomedical Imaging (ISBI), Venice, Italy.","DOI":"10.1109\/ISBI.2019.8759329"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"112178","DOI":"10.1016\/j.rse.2020.112178","article-title":"Wind direction retrieval from Sentinel-1 SAR images using ResNet","volume":"253","author":"Zanchetta","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Yin, M., Chen, Z., and Zhang, C. (2023). A CNN-Transformer Network Combining CBAM for Change Detection in High-Resolution Remote Sensing Images. Remote Sens., 15.","DOI":"10.3390\/rs15092406"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1016\/j.isprsjprs.2021.09.015","article-title":"Local climate zone classification using a multi-scale, multi-level attention network","volume":"181","author":"Kim","year":"2021","journal-title":"ISPRS-J. Photogramm. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.-Y., and Kweon, I.S. (2018, January 8\u201314). CBAM: Convolutional Block Attention Module. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"112483","DOI":"10.1016\/j.rse.2021.112483","article-title":"Deep Network Based on up and down Blocks Using Wavelet Transform and Successive Multi-Scale Spatial Attention for Cloud Detection","volume":"261","author":"Zhang","year":"2021","journal-title":"Remote Sens Environ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1597","DOI":"10.1109\/TMI.2018.2791488","article-title":"Joint Optic Disc and Cup Segmentation Based on Multi-Label Deep Network and Polar Transformation","volume":"37","author":"Fu","year":"2018","journal-title":"IEEE Trans. Med. Imaging."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"105845","DOI":"10.1016\/j.compag.2020.105845","article-title":"Automatic extraction of wheat lodging area based on transfer learning method and deeplabv3+ network","volume":"179","author":"Zhang","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"107431","DOI":"10.1016\/j.compag.2022.107431","article-title":"EAGNet: A Method for Automatic Extraction of Agricultural Greenhouses from High Spatial Resolution Remote Sensing Images Based on Hybrid Multi-Attention","volume":"202","author":"Li","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1016\/j.isprsjprs.2022.11.020","article-title":"Bridging optical and SAR satellite image time series via contrastive feature extraction for crop classification","volume":"195","author":"Yuan","year":"2023","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Chen, Y., Zhang, C., Wang, S., Li, J., Li, F., Yang, X., Wang, Y., and Yin, L. (2019). Extracting Crop Spatial Distribution from Gaofen 2 Imagery Using a Convolutional Neural Network. Appl. Sci., 9.","DOI":"10.3390\/app9142917"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/15\/3711\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:18:39Z","timestamp":1760127519000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/15\/3711"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,25]]},"references-count":53,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2023,8]]}},"alternative-id":["rs15153711"],"URL":"https:\/\/doi.org\/10.3390\/rs15153711","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,25]]}}}