{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T14:35:15Z","timestamp":1773930915437,"version":"3.50.1"},"reference-count":57,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2024,12,10]],"date-time":"2024-12-10T00:00:00Z","timestamp":1733788800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science and Technology Support Project of Sichuan Province","award":["2023YFS0366"],"award-info":[{"award-number":["2023YFS0366"]}]},{"name":"Science and Technology Support Project of Sichuan Province","award":["2024YFFK0414"],"award-info":[{"award-number":["2024YFFK0414"]}]},{"name":"Science and Technology Support Project of Sichuan Province","award":["2020YFA0608203"],"award-info":[{"award-number":["2020YFA0608203"]}]},{"name":"Key Projects of Global Change and Response of the Ministry of Science and Technology of China","award":["2023YFS0366"],"award-info":[{"award-number":["2023YFS0366"]}]},{"name":"Key Projects of Global Change and Response of the Ministry of Science and Technology of China","award":["2024YFFK0414"],"award-info":[{"award-number":["2024YFFK0414"]}]},{"name":"Key Projects of Global Change and Response of the Ministry of Science and Technology of China","award":["2020YFA0608203"],"award-info":[{"award-number":["2020YFA0608203"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Cropland is a fundamental basis for agricultural development and a prerequisite for ensuring food security. The segmentation and extraction of croplands using remote sensing images are important measures and prerequisites for detecting and protecting farmland. This study addresses the challenges of diverse image sources, multi-scale representations of cropland, and the confusion of features between croplands and other land types in large-area remote sensing image information extraction. To this end, a multi-source self-annotated dataset was developed using satellite images from GaoFen-2, GaoFen-7, and WorldView, which was integrated with public datasets GID and LoveDA to create the CRMS dataset. A novel semantic segmentation network, the Global\u2013Local Context Aggregation Network (GLCANet), was proposed. This method integrates the Bilateral Feature Encoder (BFE) of CNNs and Transformers with a global\u2013local information mining module (GLM) to enhance global context extraction and improve cropland separability. It also employs a multi-scale progressive upsampling structure (MPUS) to refine the accuracy of diverse arable land representations from multi-source imagery. To tackle the issue of inconsistent features within the cropland class, a loss function based on hard sample mining and multi-scale features was constructed. The experimental results demonstrate that GLCANet improves OA and mIoU by 3.2% and 2.6%, respectively, compared to the existing advanced networks on the CRMS dataset. Additionally, the proposed method also demonstrated high precision and practicality in segmenting large-area croplands in Chongzhou City, Sichuan Province, China.<\/jats:p>","DOI":"10.3390\/rs16244627","type":"journal-article","created":{"date-parts":[[2024,12,10]],"date-time":"2024-12-10T11:17:20Z","timestamp":1733829440000},"page":"4627","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["GLCANet: Global\u2013Local Context Aggregation Network for Cropland Segmentation from Multi-Source Remote Sensing Images"],"prefix":"10.3390","volume":"16","author":[{"given":"Jinglin","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}]},{"given":"Yuxia","family":"Li","sequence":"additional","affiliation":[{"name":"School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}]},{"given":"Zhonggui","family":"Tong","sequence":"additional","affiliation":[{"name":"School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9875-9853","authenticated-orcid":false,"given":"Lei","family":"He","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Chengdu University of Information Technology, Chengdu 610225, China"}]},{"given":"Mingheng","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Chengdu University of Information Technology, Chengdu 610225, China"}]},{"given":"Zhenye","family":"Niu","sequence":"additional","affiliation":[{"name":"School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}]},{"given":"Haiping","family":"He","sequence":"additional","affiliation":[{"name":"School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Xu, H., Jiang, L., and Liu, Y. (2024). Assessing the Accuracy and Consistency of Cropland Products in the Middle Yangtze Plain. Land, 13.","DOI":"10.3390\/land13030301"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Qu, Y., Zhang, B., Xu, H., Qiao, Z., and Liu, L. (2024). Interannual Monitoring of Cropland in South China from 1991 to 2020 Based on the Combination of Deep Learning and the LandTrendr Algorithm. Remote Sens., 16.","DOI":"10.3390\/rs16060949"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"108672","DOI":"10.1016\/j.compag.2024.108672","article-title":"CCropLand30: High-resolution hybrid cropland maps of China created through the synergy of state-of-the-art remote sensing products and the latest national land survey","volume":"218","author":"Zhang","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2392845","DOI":"10.1080\/17538947.2024.2392845","article-title":"MATNet: Multi-attention Transformer network for cropland semantic segmentation in remote sensing images","volume":"17","author":"Zhang","year":"2024","journal-title":"Int. J. Digit. Earth"},{"key":"ref_5","first-page":"4406816","article-title":"A novel knowledge-driven automated solution for high-resolution cropland extraction by cross-scale sample transfer","volume":"61","author":"Zhang","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","unstructured":"Badrinarayanan, V., Kendall, A., and SegNet, R.C. (2015). A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation. arXiv."},{"key":"ref_7","first-page":"234","article-title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","volume":"Volume 18","author":"Ronneberger","year":"2015","journal-title":"Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI 2015, Proceedings of the 18th International Conference, Munich, Germany, 5\u20139 October 2015"},{"key":"ref_8","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_9","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_10","doi-asserted-by":"crossref","unstructured":"Sun, K., Xiao, B., Liu, D., and Wang, J. (2019, January 15\u201320). Deep High-Resolution Representation Learning for Human Pose Estimation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00584"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zhou, L., Zhang, C., and Wu, M. (2018, January 18\u201322). D-LinkNet: LinkNet with Pretrained Encoder and Dilated Convolution for High Resolution Satellite Imagery Road Extraction. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00034"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"126265","DOI":"10.1016\/j.conbuildmat.2021.126265","article-title":"UNet-Based Model for Crack Detection Integrating Visual Explanations","volume":"322","author":"Liu","year":"2022","journal-title":"Constr. Build. Mater."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"6002905","DOI":"10.1109\/LGRS.2023.3243609","article-title":"Building Extraction from Very High-Resolution Remote Sensing Images Using Refine-UNet","volume":"20","author":"Qiu","year":"2023","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Jiao, L., Huo, L., Hu, C., Ping, T., and Zheng, Z. (2022). Refined UNet V4: End-to-End Patch-Wise Network for Cloud and Shadow Segmentation with Bilateral Grid. Remote Sens., 14.","DOI":"10.3390\/rs14020358"},{"key":"ref_15","first-page":"pp.5998","article-title":"Attention Is All You Need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Strudel, R., Garcia, R., Laptev, I., and Schmid, C. (2021, January 11\u201317). Segmenter: Transformer for Semantic Segmentation. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.00717"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., and Wang, M. (2022, January 23\u201327). Swin-UNet: UNet-Like Pure Transformer for Medical Image Segmentation. Proceedings of the European Conference on Computer Vision, Online.","DOI":"10.1007\/978-3-031-25066-8_9"},{"key":"ref_18","first-page":"6005005","article-title":"SDSC-UNet: Dual Skip Connection ViT-Based U-Shaped Model for Building Extraction","volume":"20","author":"Zhang","year":"2023","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Yan, X., Tang, H., Sun, S., Ma, H., Kong, D., and Xie, X. (2022, January 3\u20138). After-UNet: Axial Fusion Transformer UNet for Medical Image Segmentation. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, Waikoloa, HI, USA.","DOI":"10.1109\/WACV51458.2022.00333"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Fan, C.M., Liu, T.J., and Liu, K.H. (June, January 28). SUNet: Swin Transformer UNet for Image Denoising. Proceedings of the 2022 IEEE International Symposium on Circuits and Systems (ISCAS), Austin, TX, USA.","DOI":"10.1109\/ISCAS48785.2022.9937486"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Liu, Y., Minh Nguyen, D., Deligiannis, N., Ding, W., and Munteanu, A. (2017). Hourglass-Shapenetwork Based Semantic Segmentation for High Resolution Aerial Imagery. Remote Sens., 9.","DOI":"10.3390\/rs9060522"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3368","DOI":"10.1080\/2150704X.2015.1062157","article-title":"On Combining Multiscale Deep Learning Features for the Classification of Hyperspectral Remote Sensing Imagery","volume":"36","author":"Zhao","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.isprsjprs.2016.01.004","article-title":"Learning Multiscale and Deep Representations for Classifying Remotely Sensed Imagery","volume":"113","author":"Zhao","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.isprsjprs.2017.07.014","article-title":"A Hybrid MLP-CNN Classifier for Very Fine Resolution Remotely Sensed Image Classification","volume":"140","author":"Zhang","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Paisitkriangkrai, S., Sherrah, J., Janney, P., and Hengel, A.V.D. (2015, January 7\u201312). Effective Semantic Pixel Labelling with Convolutional Networks and Conditional Random Fields. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Boston, MA, USA.","DOI":"10.1109\/CVPRW.2015.7301381"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Audebert, N., Le Saux, B., and Lefevre, S. (2016, January 10\u201315). How Useful Is Region-Based Classification of Remote Sensing Images in a Deep Learning Framework?. Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China.","DOI":"10.1109\/IGARSS.2016.7730327"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2868","DOI":"10.1109\/JSTARS.2016.2582921","article-title":"Semantic Labeling of Aerial and Satellite Imagery","volume":"9","author":"Paisitkriangkrai","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_28","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":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"778","DOI":"10.1109\/LGRS.2017.2681128","article-title":"Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data","volume":"14","author":"Kussul","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"881","DOI":"10.1109\/TGRS.2016.2616585","article-title":"Dense Semantic Labeling of Subdecimeter Resolution Images with Convolutional Neural Networks","volume":"55","author":"Volpi","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.isprsjprs.2022.06.008","article-title":"UNetformer: A UNet-Like Transformer for Efficient Semantic Segmentation of Remote Sensing Urban Scene Imagery","volume":"190","author":"Wang","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Scheibenreif, L., Hanna, J., Mommert, M., and Borth, D. (2022, January 18\u201324). Self-Supervised Vision Transformers for Land-Cover Segmentation and Classification. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPRW56347.2022.00148"},{"key":"ref_33","first-page":"1","article-title":"Looking Outside the Window: Wide-Context Transformer for the Semantic Segmentation of High-Resolution Remote Sensing Images","volume":"60","author":"Ding","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Yang, S., Zhang, L., Liu, S., Lu, H., and Chen, H. (IEEE Trans. Neural Netw. Learn. Syst., 2023). Real-Time Semantic Segmentation via a Densely Aggregated Bilateral Network, IEEE Trans. Neural Netw. Learn. Syst., in press.","DOI":"10.1109\/TNNLS.2023.3326665"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"3273","DOI":"10.1109\/TMM.2022.3157995","article-title":"FBSNet: A Fast Bilateral Symmetrical Network for Real-Time Semantic Segmentation","volume":"25","author":"Gao","year":"2023","journal-title":"IEEE Trans. Multimed."},{"key":"ref_36","first-page":"61","article-title":"UTNet: A Hybrid Transformer Architecture for Medical Image Segmentation","volume":"Volume 24","author":"Gao","year":"2021","journal-title":"Medical Image Computing and Computer Assisted Intervention\u2014MICCAI 2021, Proceedings of the 24th International Conference, Strasbourg, France, 27 September\u20131 October 2021"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Ma, X., Ma, M., Hu, C., Song, Z., Zhao, Z., Feng, T., and Zhang, W. (2023, January 4\u201310). Log-CAN: Local-Global Class-Aware Network for Semantic Segmentation of Remote Sensing Images. Proceedings of the ICASSP 2023\u20142023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Rhodes Island, Greece.","DOI":"10.1109\/ICASSP49357.2023.10095835"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2505205","DOI":"10.1109\/LGRS.2023.3318348","article-title":"Global Context Dependencies Aware Network for Efficient Semantic Segmentation of Fine-Resolution Remote Sensing Images","volume":"20","author":"Cui","year":"2023","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Dong, X., Bao, J., Chen, D., Zhang, W., Yu, N., Yuan, L., Chen, D., and Guo, B. (2022, January 18\u201324). CSWin Transformer: A General Vision Transformer Backbone with Cross-Shaped Windows. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LO, USA.","DOI":"10.1109\/CVPR52688.2022.01181"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., and Sang, N. (2018, January 8\u201314). BiSeNet: Bilateral Segmentation Network for Real-Time Semantic Segmentation. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01261-8_20"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Zheng, Z., Zhong, Y., Wang, J., and Ma, A. (2020, January 13\u201319). Foreground-Aware Relation Network for Geospatial Object Segmentation in High Spatial Resolution Remote Sensing Imagery. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00415"},{"key":"ref_42","unstructured":"Poudel, R.P.K., Bonde, U., Liwicki, S., and Zach, C. (2018). ContextNet: Exploring Context and Detail for Semantic Segmentation in Real-Time. arXiv."},{"key":"ref_43","unstructured":"Poudel, R.P., Liwicki, S., and Cipolla, R. (2019). Fast-SCNN: Fast Semantic Segmentation Network. arXiv."},{"key":"ref_44","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_45","unstructured":"Wang, J., Zheng, Z., Ma, A., Lu, X., and Zhong, Y. (2021). LOVEDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation. arXiv."},{"key":"ref_46","unstructured":"Loshchilov, I., and Hutter, F. (2017). Decoupled Weight Decay Regularization. arXiv."},{"key":"ref_47","first-page":"12077","article-title":"SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers","volume":"34","author":"Xie","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_48","first-page":"5607713","article-title":"Multiattention Network for Semantic Segmentation of Fine-Resolution Remote Sensing Images","volume":"60","author":"Li","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1131","DOI":"10.1080\/01431161.2022.2030071","article-title":"A2-FPN for Semantic Segmentation of Fine-Resolution Remotely Sensed Images","volume":"43","author":"Li","year":"2022","journal-title":"Int. J. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.isprsjprs.2021.09.005","article-title":"ABCNet: Attentive Bilateral Contextual Network for Efficient Semantic Segmentation of Fine-Resolution Remotely Sensed Imagery","volume":"181","author":"Li","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Wang, L., Li, R., Wang, D., Duan, C., Wang, T., and Meng, X. (2021). Transformer Meets Convolution: A Bilateral Awareness Network for Semantic Segmentation of Very Fine Resolution Urban Scene Images. Remote Sens., 13.","DOI":"10.3390\/rs13163065"},{"key":"ref_52","first-page":"6506105","article-title":"A Novel Transformer-Based Semantic Segmentation Scheme for Fine-Resolution Remote Sensing Images","volume":"19","author":"Wang","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Li, Z., Wang, Y., Tian, F., Zhang, J., Chen, Y., and Li, K. (2024). BAFormer: A Novel Boundary-Aware Compensation UNet-like Transformer for High-Resolution Cropland Extraction. Remote Sens., 16.","DOI":"10.20944\/preprints202406.0053.v1"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D. (2017, January 22\u201329). Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.74"},{"key":"ref_55","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_56","doi-asserted-by":"crossref","unstructured":"Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B. (2022, January 18\u201324). High-Resolution Image Synthesis with Latent Diffusion Models. Proceedings of the 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"ref_57","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."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/24\/4627\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:51:42Z","timestamp":1760115102000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/24\/4627"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,10]]},"references-count":57,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["rs16244627"],"URL":"https:\/\/doi.org\/10.3390\/rs16244627","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,10]]}}}