{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T09:55:31Z","timestamp":1768730131016,"version":"3.49.0"},"reference-count":82,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,4,23]],"date-time":"2023-04-23T00:00:00Z","timestamp":1682208000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U1901601"],"award-info":[{"award-number":["U1901601"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Accurate cropland information is crucial for the assessment of food security and the formulation of effective agricultural policies. Extracting cropland from remote sensing imagery is challenging due to spectral diversity and mixed pixels. Recent advances in remote sensing technology have facilitated the availability of very high-resolution (VHR) remote sensing images that provide detailed ground information. However, VHR cropland extraction in southern China is difficult because of the high heterogeneity and fragmentation of cropland and the insufficient observations of VHR sensors. To address these challenges, we proposed a deep learning-based method for automated high-resolution cropland extraction. The method used an improved HRRS-U-Net model to accurately identify the extent of cropland and explicitly locate field boundaries. The HRRS-U-Net maintained high-resolution details throughout the network to generate precise cropland boundaries. Additionally, the residual learning (RL) and the channel attention mechanism (CAM) were introduced to extract deeper discriminative representations. The proposed method was evaluated over four city-wide study areas (Qingyuan, Yangjiang, Guangzhou, and Shantou) with a diverse range of agricultural systems, using GaoFen-2 (GF-2) images. The cropland extraction results for the study areas had an overall accuracy (OA) ranging from 97.00% to 98.33%, with F1 scores (F1) of 0.830\u20130.940 and Kappa coefficients (Kappa) of 0.814\u20130.929. The OA was 97.85%, F1 was 0.915, and Kappa was 0.901 over all study areas. Moreover, our proposed method demonstrated advantages compared to machine learning methods (e.g., RF) and previous semantic segmentation models, such as U-Net, U-Net++, U-Net3+, and MPSPNet. The results demonstrated the generalization ability and reliability of the proposed method for cropland extraction in southern China using VHR remote images.<\/jats:p>","DOI":"10.3390\/rs15092231","type":"journal-article","created":{"date-parts":[[2023,4,24]],"date-time":"2023-04-24T02:06:11Z","timestamp":1682301971000},"page":"2231","update-policy":"https:\/\/linproxy.fan.workers.dev:443\/https\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Cropland Extraction in Southern China from Very High-Resolution Images Based on Deep Learning"],"prefix":"10.3390","volume":"15","author":[{"given":"Dehua","family":"Xie","sequence":"first","affiliation":[{"name":"Guangdong Provincial Key Laboratory of Land Use and Consolidation, South China Agricultural University, Guangzhou 510642, China"}]},{"given":"Han","family":"Xu","sequence":"additional","affiliation":[{"name":"Guangdong Provincial Key Laboratory of Land Use and Consolidation, South China Agricultural University, Guangzhou 510642, China"}]},{"given":"Xiliu","family":"Xiong","sequence":"additional","affiliation":[{"name":"Institute of Ecological Environment Protection, Guangxi Eco-Engineering Vocational and Technical College, Liuzhou 545004, China"}]},{"given":"Min","family":"Liu","sequence":"additional","affiliation":[{"name":"Guangdong Provincial Key Laboratory of Land Use and Consolidation, South China Agricultural University, Guangzhou 510642, China"}]},{"given":"Haoran","family":"Hu","sequence":"additional","affiliation":[{"name":"Guangdong Provincial Key Laboratory of Land Use and Consolidation, South China Agricultural University, Guangzhou 510642, China"}]},{"given":"Mengsen","family":"Xiong","sequence":"additional","affiliation":[{"name":"Guangdong Provincial Key Laboratory of Land Use and Consolidation, South China Agricultural University, Guangzhou 510642, China"}]},{"ORCID":"https:\/\/linproxy.fan.workers.dev:443\/https\/orcid.org\/0000-0003-1942-931X","authenticated-orcid":false,"given":"Luo","family":"Liu","sequence":"additional","affiliation":[{"name":"Guangdong Provincial Key Laboratory of Land Use and Consolidation, South China Agricultural University, Guangzhou 510642, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"111624","DOI":"10.1016\/j.rse.2019.111624","article-title":"Mapping Cropping Intensity in China Using Time Series Landsat and Sentinel-2 Images and Google Earth Engine","volume":"239","author":"Liu","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"150718","DOI":"10.1016\/j.scitotenv.2021.150718","article-title":"Agricultural Land Systems Importance for Supporting Food Security and Sustainable Development Goals: A Systematic Review","volume":"806","author":"Viana","year":"2022","journal-title":"Sci. Total Environ."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Di, Y., Zhang, G., You, N., Yang, T., Zhang, Q., Liu, R., Doughty, R.B., and Zhang, Y. (2021). Mapping Croplands in the Granary of the Tibetan Plateau Using All Available Landsat Imagery, A Phenology-Based Approach, and Google Earth Engine. Remote Sens., 13.","DOI":"10.3390\/rs13122289"},{"key":"ref_4","first-page":"82","article-title":"Roadside Collection of Training Data for Cropland Mapping Is Viable When Environmental and Management Gradients Are Surveyed","volume":"80","author":"Waldner","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"442","DOI":"10.1016\/j.rse.2005.08.012","article-title":"Spatial and Temporal Patterns of China\u2019s Cropland during 1990\u20132000: An Analysis Based on Landsat TM Data","volume":"98","author":"Liu","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Wang, X., Yan, F., and Su, F. (2020). Impacts of Urbanization on the Ecosystem Services in the Guangdong-Hong Kong-Macao Greater Bay Area, China. Remote Sens., 12.","DOI":"10.3390\/rs12193269"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1007\/s11442-015-1150-6","article-title":"Impact of Farmland Changes on Production Potential in China during 1990\u20132010","volume":"25","author":"Liu","year":"2015","journal-title":"J. Geogr. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1038\/s43016-021-00429-z","article-title":"Global Maps of Cropland Extent and Change Show Accelerated Cropland Expansion in the Twenty-First Century","volume":"3","author":"Potapov","year":"2022","journal-title":"Nat. Food"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1980","DOI":"10.1111\/gcb.12838","article-title":"Mapping Global Cropland and Field Size","volume":"21","author":"Fritz","year":"2015","journal-title":"Glob. Change Biol."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Hao, P., L\u00f6w, F., and Biradar, C. (2018). Annual Cropland Mapping Using Reference Landsat Time Series\u2014A Case Study in Central Asia. Remote Sens., 10.","DOI":"10.3390\/rs10122057"},{"key":"ref_11","first-page":"110","article-title":"Mapping Cropland Extent of Southeast and Northeast Asia Using Multi-Year Time-Series Landsat 30-m Data Using a Random Forest Classifier on the Google Earth Engine Cloud","volume":"81","author":"Oliphant","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Htitiou, A., Boudhar, A., Chehbouni, A., and Benabdelouahab, T. (2021). National-Scale Cropland Mapping Based on Phenological Metrics, Environmental Covariates, and Machine Learning on Google Earth Engine. Remote Sens., 13.","DOI":"10.3390\/rs13214378"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1959","DOI":"10.1080\/01431160412331291297","article-title":"GLC2000: A New Approach to Global Land Cover Mapping from Earth Observation Data","volume":"26","author":"Belward","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_14","unstructured":"Friedl, M., and Sulla-Menashe, D. (2019). MCD12Q1 MODIS\/Terra+Aqua Land Cover Type Yearly L3 Global 500m SIN Grid V006, NASA."},{"key":"ref_15","unstructured":"Friedl, M., Gray, J., and Sulla-Menashe, D. (2019). MCD12Q2 MODIS\/Terra+Aqua Land Cover Dynamics Yearly L3 Global 500m SIN Grid V006, NASA."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Buchhorn, M., Lesiv, M., Tsendbazar, N.-E., Herold, M., Bertels, L., and Smets, B. (2020). Copernicus Global Land Cover Layers\u2014Collection 2. Remote Sens., 12.","DOI":"10.3390\/rs12061044"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1080\/17538947.2013.822574","article-title":"FROM-GC: 30 m Global Cropland Extent Derived through Multisource Data Integration","volume":"6","author":"Yu","year":"2013","journal-title":"Int. J. Digit. Earth"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3907","DOI":"10.5194\/essd-13-3907-2021","article-title":"The 30 m Annual Land Cover Dataset and Its Dynamics in China from 1990 to 2019","volume":"13","author":"Yang","year":"2021","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1038\/s41597-022-01307-4","article-title":"Dynamic World, Near Real-Time Global 10 m Land Use Land Cover Mapping","volume":"9","author":"Brown","year":"2022","journal-title":"Sci. Data"},{"key":"ref_20","unstructured":"Panda, S.S., Rao, M.N., Thenkabail, P., and Fitzerald, J.E. (2015). Remotely Sensed Data Characterization, Classification, and Accuracies, CRC Press."},{"key":"ref_21","first-page":"102557","article-title":"Automated Delineation of Agricultural Field Boundaries from Sentinel-2 Images Using Recurrent Residual U-Net","volume":"105","author":"Zhang","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.isprsjprs.2017.01.016","article-title":"Accuracy Assessment of Seven Global Land Cover Datasets over China","volume":"125","author":"Yang","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"106946","DOI":"10.1016\/j.compag.2022.106946","article-title":"Quantifying the Accuracies of Six 30-m Cropland Datasets over China: A Comparison and Evaluation Analysis","volume":"197","author":"Zhang","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Xiong, J., Thenkabail, P.S., Tilton, J.C., Gumma, M.K., Teluguntla, P., Oliphant, A., Congalton, R.G., Yadav, K., and Gorelick, N. (2017). Nominal 30-m Cropland Extent Map of Continental Africa by Integrating Pixel-Based and Object-Based Algorithms Using Sentinel-2 and Landsat-8 Data on Google Earth Engine. Remote Sens., 9.","DOI":"10.3390\/rs9101065"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"111912","DOI":"10.1016\/j.rse.2020.111912","article-title":"A Generalized Approach Based on Convolutional Neural Networks for Large Area Cropland Mapping at Very High Resolution","volume":"247","author":"Zhang","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Lu, R., Wang, N., Zhang, Y., Lin, Y., Wu, W., and Shi, Z. (2022). Extraction of Agricultural Fields via DASFNet with Dual Attention Mechanism and Multi-Scale Feature Fusion in South Xinjiang, China. Remote Sens., 14.","DOI":"10.3390\/rs14092253"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Xu, L., Ming, D., Zhou, W., Bao, H., Chen, Y., and Ling, X. (2019). Farmland Extraction from High Spatial Resolution Remote Sensing Images Based on Stratified Scale Pre-Estimation. Remote Sens., 11.","DOI":"10.3390\/rs11020108"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Cai, Z., Hu, Q., Zhang, X., Yang, J., Wei, H., He, Z., Song, Q., Wang, C., Yin, G., and Xu, B. (2022). An Adaptive Image Segmentation Method with Automatic Selection of Optimal Scale for Extracting Cropland Parcels in Smallholder Farming Systems. Remote Sens., 14.","DOI":"10.3390\/rs14133067"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"108961","DOI":"10.1016\/j.ecolind.2022.108961","article-title":"A Multi-Angle Comprehensive Solution Based on Deep Learning to Extract Cultivated Land Information from High-Resolution Remote Sensing Images","volume":"141","author":"Liu","year":"2022","journal-title":"Ecol. Indic."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"6690","DOI":"10.1109\/TGRS.2019.2907932","article-title":"Deep Learning for Hyperspectral Image Classification: An Overview","volume":"57","author":"Li","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","first-page":"102937","article-title":"Large-Area Mapping of Active Cropland and Short-Term Fallows in Smallholder Landscapes Using PlanetScope Data","volume":"112","author":"Rufin","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1016\/S2095-3119(15)61321-1","article-title":"How Do Temporal and Spectral Features Matter in Crop Classification in Heilongjiang Province, China?","volume":"16","author":"Hu","year":"2017","journal-title":"J. Integr. Agric."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"112831","DOI":"10.1016\/j.rse.2021.112795","article-title":"Mapping of Crop Types and Crop Sequences with Combined Time Series of Sentinel-1, Sentinel-2 and Landsat 8 Data for Germany","volume":"269","author":"Schwieder","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"875","DOI":"10.3390\/rs1040875","article-title":"Supervised Classification of Agricultural Land Cover Using a Modified K-NN Technique (MNN) and Landsat Remote Sensing Imagery","volume":"1","author":"Samaniego","year":"2009","journal-title":"Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2015.09.013","article-title":"Automated Annual Cropland Mapping Using Knowledge-Based Temporal Features","volume":"110","author":"Waldner","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1038\/s41597-022-01169-w","article-title":"Validation and Refinement of Cropland Data Layer Using a Spatial-Temporal Decision Tree Algorithm","volume":"9","author":"Lin","year":"2022","journal-title":"Sci. Data"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1016\/j.isprsjprs.2018.07.017","article-title":"A 30-m Landsat-Derived Cropland Extent Product of Australia and China Using Random Forest Machine Learning Algorithm on Google Earth Engine Cloud Computing Platform","volume":"144","author":"Teluguntla","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Liu, R., Tao, F., Liu, X., Na, J., Leng, H., Wu, J., and Zhou, T. (2022). RAANet: A Residual ASPP with Attention Framework for Semantic Segmentation of High-Resolution Remote Sensing Images. Remote Sens., 14.","DOI":"10.3390\/rs14133109"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Wang, M., Wang, J., Cui, Y., Liu, J., and Chen, L. (2022). Agricultural Field Boundary Delineation with Satellite Image Segmentation for High-Resolution Crop Mapping: A Case Study of Rice Paddy. Agronomy, 12.","DOI":"10.3390\/agronomy12102342"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.isprsjprs.2017.01.019","article-title":"Automated Cropland Mapping of Continental Africa Using Google Earth Engine Cloud Computing","volume":"126","author":"Xiong","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.rse.2016.02.016","article-title":"Mapping Paddy Rice Planting Area in Northeastern Asia with Landsat 8 Images, Phenology-Based Algorithm and Google Earth Engine","volume":"185","author":"Dong","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Guo, Y., Xia, H., Pan, L., Zhao, X., Li, R., Bian, X., Wang, R., and Yu, C. (2021). Development of a New Phenology Algorithm for Fine Mapping of Cropping Intensity in Complex Planting Areas Using Sentinel-2 and Google Earth Engine. ISPRS Int. J. Geo-Inf., 10.","DOI":"10.3390\/ijgi10090587"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Zheng, J., Liu, L., Chen, H., Gou, Y., Che, Y., Xu, H., and Li, Q. (2019). Characteristics of Warm Clouds and Precipitation in South China during the Pre-Flood Season Using Datasets from a Cloud Radar, a Ceilometer, and a Disdrometer. Remote Sens., 11.","DOI":"10.3390\/rs11243045"},{"key":"ref_44","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_45","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1016\/j.isprsjprs.2021.01.020","article-title":"Remote Sensing Image Segmentation Advances: A Meta-Analysis","volume":"173","author":"Kotaridis","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1162\/neco.1989.1.4.541","article-title":"Backpropagation Applied to Handwritten Zip Code Recognition","volume":"1","author":"LeCun","year":"1989","journal-title":"Neural Comput."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"5570","DOI":"10.1038\/s41598-022-09451-y","article-title":"Contextual Associations Represented Both in Neural Networks and Human Behavior","volume":"12","author":"Aminoff","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Qing, Y., and Liu, W. (2021). Hyperspectral Image Classification Based on Multi-Scale Residual Network with Attention Mechanism. Remote Sens., 13.","DOI":"10.3390\/rs13030335"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Xu, W., Deng, X., Guo, S., Chen, J., Sun, L., Zheng, X., Xiong, Y., Shen, Y., and Wang, X. (2020). High-Resolution U-Net: Preserving Image Details for Cultivated Land Extraction. Sensors, 20.","DOI":"10.3390\/s20154064"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Shi, H., Cao, G., Zhang, Y., Ge, Z., Liu, Y., and Fu, P. (2022). H2A2Net: A Hybrid Convolution and Hybrid Resolution Network with Double Attention for Hyperspectral Image Classification. Remote Sens., 14.","DOI":"10.3390\/rs14174235"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Mei, X., Pan, E., Ma, Y., Dai, X., Huang, J., Fan, F., Du, Q., Zheng, H., and Ma, J. (2019). Spectral-Spatial Attention Networks for Hyperspectral Image Classification. Remote Sens., 11.","DOI":"10.3390\/rs11080963"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"3349","DOI":"10.1109\/TPAMI.2020.2983686","article-title":"Deep High-Resolution Representation Learning for Visual Recognition","volume":"43","author":"Wang","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_53","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 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_54","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_55","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_56","doi-asserted-by":"crossref","first-page":"15473","DOI":"10.1038\/s41598-022-19831-z","article-title":"Transformer Based on Channel-Spatial Attention for Accurate Classification of Scenes in Remote Sensing Image","volume":"12","author":"Guo","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"122798","DOI":"10.1109\/ACCESS.2020.3007719","article-title":"Channel-Attention U-Net: Channel Attention Mechanism for Semantic Segmentation of Esophagus and Esophageal Cancer","volume":"8","author":"Huang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Navab, N., Hornegger, J., Wells, W.M., and Frangi, A.F. (2015, January 5\u20139). U-Net: Convolutional Networks for Biomedical Image Segmentation. Proceedings of the Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI 2015, Munich, Germany.","DOI":"10.1007\/978-3-319-24553-9"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"3549","DOI":"10.1109\/IGARSS.2005.1526613","article-title":"A New Method for Atmospheric Correction and Aerosol Optical Property Retrieval for VIS-SWIR Multi- and Hyperspectral Imaging Sensors: QUAC (QUick Atmospheric Correction)","volume":"Volume 5","author":"Bernstein","year":"2005","journal-title":"Proceedings of the 2005 IEEE International Geoscience and Remote Sensing SymposiumIGARSS \u201905"},{"key":"ref_60","unstructured":"Zhang, Y. Problems in the Fusion of Commercial High-Resolution Satellites Images as Well as LANDSAT 7 Images and Initial Solutions. Proceedings of the Proceedings of the ISPRS, CIG, and SDH Joint International Symposium on Geospatial Theory, Processing and Applications,, Ottawa, ON, Canada, 9\u201312 July 2002."},{"key":"ref_61","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_62","first-page":"562","article-title":"Deeply-Supervised Nets","volume":"Volume 38","author":"Lebanon","year":"2015","journal-title":"Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Li, S., Wan, L., Tang, L., and Zhang, Z. (2022). MFEAFN: Multi-Scale Feature Enhanced Adaptive Fusion Network for Image Semantic Segmentation. PLoS ONE, 17.","DOI":"10.1371\/journal.pone.0274249"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., and Ahmadi, S.-A. (2016, January 25\u201328). V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation. Proceedings of the 2016 Fourth International Conference on 3D Vision (3DV), Stanford, CA, USA.","DOI":"10.1109\/3DV.2016.79"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"102026","DOI":"10.1016\/j.compmedimag.2021.102026","article-title":"Unified Focal Loss: Generalising Dice and Cross Entropy-Based Losses to Handle Class Imbalanced Medical Image Segmentation","volume":"95","author":"Yeung","year":"2022","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_66","unstructured":"Cortes, C., Mohri, M., and Rostamizadeh, A. (2012). L2 Regularization for Learning Kernels. arXiv."},{"key":"ref_67","unstructured":"Kingma, D., and Ba, J. (2014, January 14\u201316). Adam: A Method for Stochastic Optimization. Proceedings of the International Conference on Learning Representations, Banff, AB, Canada."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015, January 11\u201318). Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV) Las Condes, Chile.","DOI":"10.1109\/ICCV.2015.123"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.rse.2014.02.015","article-title":"Good Practices for Estimating Area and Assessing Accuracy Of Land Change","volume":"148","author":"Olofsson","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Alganci, U. (2019). Dynamic Land Cover Mapping of Urbanized Cities with Landsat 8 Multi-Temporal Images: Comparative Evaluation of Classification Algorithms and Dimension Reduction Methods. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8030139"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Congalton, R.G., and Green, K. (2008). Assessing the Accuracy of Remotely Sensed Data, CRC Press.","DOI":"10.1201\/9781420055139"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.isprsjprs.2018.04.002","article-title":"A Review of Accuracy Assessment for Object-Based Image Analysis: From per-Pixel to per-Polygon Approaches","volume":"141","author":"Ye","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1856","DOI":"10.1109\/TMI.2019.2959609","article-title":"Unet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation","volume":"39","author":"Zhou","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Huang, H., Lin, L., Tong, R., Hu, H., Zhang, Q., Iwamoto, Y., Han, X., Chen, Y.-W., and Wu, J. (2020, January 4\u20138). UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation. Proceedings of the ICASSP 2020\u20142020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain.","DOI":"10.1109\/ICASSP40776.2020.9053405"},{"key":"ref_75","first-page":"103193","article-title":"National High-Resolution Cropland Classification of Japan with Agricultural Census Information and Multi-Temporal Multi-Modality Datasets","volume":"117","author":"Xia","year":"2023","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_76","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_77","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 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.isprsjprs.2013.02.009","article-title":"Field-Based Sub-Boundary Extraction from Remote Sensing Imagery Using Perceptual Grouping","volume":"79","author":"Turker","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Shrestha, S., and Vanneschi, L. (2018). Improved Fully Convolutional Network with Conditional Random Fields for Building Extraction. Remote Sens., 10.","DOI":"10.3390\/rs10071135"},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Li, W., Gong, W., Wang, Z., and Sun, J. (2020). An Improved Boundary-Aware Perceptual Loss for Building Extraction from VHR Images. Remote Sens., 12.","DOI":"10.3390\/rs12071195"},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Wang, C., Qiu, X., Huan, H., Wang, S., Zhang, Y., Chen, X., and He, W. (2021). Earthquake-Damaged Buildings Detection in Very High-Resolution Remote Sensing Images Based on Object Context and Boundary Enhanced Loss. Remote Sens., 13.","DOI":"10.3390\/rs13163119"},{"key":"ref_82","first-page":"2441","article-title":"UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-Wise Perspective with Transformer","volume":"36","author":"Wang","year":"2022","journal-title":"Proc. Conf. AAAI Artif. Intell."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/www.mdpi.com\/2072-4292\/15\/9\/2231\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:21:38Z","timestamp":1760124098000},"score":1,"resource":{"primary":{"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/www.mdpi.com\/2072-4292\/15\/9\/2231"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,23]]},"references-count":82,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["rs15092231"],"URL":"https:\/\/linproxy.fan.workers.dev:443\/https\/doi.org\/10.3390\/rs15092231","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,23]]}}}