{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T05:33:54Z","timestamp":1774935234590,"version":"3.50.1"},"reference-count":72,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2023,7,6]],"date-time":"2023-07-06T00:00:00Z","timestamp":1688601600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Joint Research Project for Meteorological Capacity Improvement","award":["22NLTSZ004"],"award-info":[{"award-number":["22NLTSZ004"]}]},{"name":"Joint Research Project for Meteorological Capacity Improvement","award":["CMSA2023MB021"],"award-info":[{"award-number":["CMSA2023MB021"]}]},{"name":"Joint Research Project for Meteorological Capacity Improvement","award":["CXFZ2022J068"],"award-info":[{"award-number":["CXFZ2022J068"]}]},{"name":"Meteorological Science and Technology Innovation Platform of China Meteorological Service Association","award":["22NLTSZ004"],"award-info":[{"award-number":["22NLTSZ004"]}]},{"name":"Meteorological Science and Technology Innovation Platform of China Meteorological Service Association","award":["CMSA2023MB021"],"award-info":[{"award-number":["CMSA2023MB021"]}]},{"name":"Meteorological Science and Technology Innovation Platform of China Meteorological Service Association","award":["CXFZ2022J068"],"award-info":[{"award-number":["CXFZ2022J068"]}]},{"name":"China Meteorological Administration Special Foundation for Innovation and Development","award":["22NLTSZ004"],"award-info":[{"award-number":["22NLTSZ004"]}]},{"name":"China Meteorological Administration Special Foundation for Innovation and Development","award":["CMSA2023MB021"],"award-info":[{"award-number":["CMSA2023MB021"]}]},{"name":"China Meteorological Administration Special Foundation for Innovation and Development","award":["CXFZ2022J068"],"award-info":[{"award-number":["CXFZ2022J068"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Accurate assessment of the extent of crop distribution and mapping different crop types are essential for monitoring and managing modern agriculture. Medium and high spatial resolution remote sensing (RS) for Earth observation and deep learning (DL) constitute one of the most major and effective tools for crop mapping. In this study, we used high-resolution Sentinel-2 imagery from Google Earth Engine (GEE) to map paddy rice and winter wheat in the Bengbu city of Anhui Province, China. We compared the performance of different popular DL backbone networks with the traditional machine learning (ML) methods, including HRNet, MobileNet, Xception, and Swin Transformer, within the improved DeepLabv3+ architecture, Segformer and random forest (RF). The results showed that the Segformer based on the combination of the Transformer architecture encoder and the lightweight multilayer perceptron (MLP) decoder achieved an overall accuracy (OA) value of 91.06%, a mean F1 Score (mF1) value of 89.26% and a mean Intersection over Union (mIoU) value of 80.70%. The Segformer outperformed other DL methods by combining the results of multiple evaluation metrics. Except for Swin Transformer, which was slightly lower than RF in OA, all DL methods significantly outperformed RF methods in accuracy for the main mapping objects, with mIoU improving by about 13.5~26%. The predicted images of paddy rice and winter wheat from the Segformer were characterized by high mapping accuracy, clear field edges, distinct detail features and a low false classification rate. Consequently, DL is an efficient option for fast and accurate mapping of paddy rice and winter wheat based on RS imagery.<\/jats:p>","DOI":"10.3390\/rs15133417","type":"journal-article","created":{"date-parts":[[2023,7,7]],"date-time":"2023-07-07T02:28:46Z","timestamp":1688696926000},"page":"3417","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Bi-Objective Crop Mapping from Sentinel-2 Images Based on Multiple Deep Learning Networks"],"prefix":"10.3390","volume":"15","author":[{"given":"Weicheng","family":"Song","sequence":"first","affiliation":[{"name":"Collaborative Innovation Center on Forecast and Evaluation of Metcorological Disasters, Nanjing University of Information Science & Technology (NUIST), Nanjing 210044, China"}]},{"given":"Aiqing","family":"Feng","sequence":"additional","affiliation":[{"name":"China Meteorological Administration Key Laboratory for Climate Prediction Studies, National Climate Center, Beijing 100081, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8613-0003","authenticated-orcid":false,"given":"Guojie","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Remote Sensing & Geomatics Engineering, Nanjing University of Information Science & Technology (NUIST), Nanjing 210044, China"}]},{"given":"Qixia","family":"Zhang","sequence":"additional","affiliation":[{"name":"Collaborative Innovation Center on Forecast and Evaluation of Metcorological Disasters, Nanjing University of Information Science & Technology (NUIST), Nanjing 210044, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7424-644X","authenticated-orcid":false,"given":"Wen","family":"Dai","sequence":"additional","affiliation":[{"name":"Collaborative Innovation Center on Forecast and Evaluation of Metcorological Disasters, Nanjing University of Information Science & Technology (NUIST), Nanjing 210044, China"}]},{"given":"Xikun","family":"Wei","sequence":"additional","affiliation":[{"name":"Collaborative Innovation Center on Forecast and Evaluation of Metcorological Disasters, Nanjing University of Information Science & Technology (NUIST), Nanjing 210044, China"}]},{"given":"Yifan","family":"Hu","sequence":"additional","affiliation":[{"name":"Collaborative Innovation Center on Forecast and Evaluation of Metcorological Disasters, Nanjing University of Information Science & Technology (NUIST), Nanjing 210044, China"}]},{"given":"Solomon Obiri Yeboah","family":"Amankwah","sequence":"additional","affiliation":[{"name":"School of Remote Sensing & Geomatics Engineering, Nanjing University of Information Science & Technology (NUIST), Nanjing 210044, China"}]},{"given":"Feihong","family":"Zhou","sequence":"additional","affiliation":[{"name":"Collaborative Innovation Center on Forecast and Evaluation of Metcorological Disasters, Nanjing University of Information Science & Technology (NUIST), Nanjing 210044, China"}]},{"given":"Yi","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Civil and Environmental Engineering, University of New South Wales (UNSW), Sydney 2052, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2628","DOI":"10.1016\/S2095-3119(19)62615-8","article-title":"Winter wheat identification by integrating spectral and temporal information derived from multi-resolution remote sensing data","volume":"18","author":"Zhang","year":"2019","journal-title":"J. Integr. Agric."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Wang, Y.M., Zhang, Z., Feng, L.W., Du, Q.Y., and Runge, T. (2020). Combining Multi-Source Data and Machine Learning Approaches to Predict Winter Wheat Yield in the Conterminous United States. Remote Sens., 12.","DOI":"10.3390\/rs12081232"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1016\/j.isprsjprs.2021.06.018","article-title":"An enhanced pixel-based phenological feature for accurate paddy rice mapping with Sentinel-2 imagery in Google Earth Engine","volume":"178","author":"Ni","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Li, S.L., Li, F.J., Gao, M.F., Li, Z.L., Leng, P., Duan, S.B., and Ren, J.Q. (2021). A New Method for Winter Wheat Mapping Based on Spectral Reconstruction Technology. Remote Sens., 13.","DOI":"10.3390\/rs13091810"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Huang, Q., Wu, W., Zhang, L., and Li, D. (2010, January 28\u201331). MODIS-NDVI-based crop growth monitoring in China agriculture remote sensing monitoring system. Proceedings of the 2010 Second IITA International Conference on Geoscience and Remote Sensing, Qingdao, China.","DOI":"10.1109\/IITA-GRS.2010.5603948"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"He, S., Peng, P., Chen, Y.Y., and Wang, X.M. (2022). Multi-Crop Classification Using Feature Selection-Coupled Machine Learning Classifiers Based on Spectral, Textural and Environmental Features. Remote Sens., 14.","DOI":"10.3390\/rs14133153"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Liu, J.H., Zhu, W.Q., Atzberger, C., Zhao, A.Z., Pan, Y.Z., and Huang, X. (2018). A Phenology-Based Method to Map Cropping Patterns under a Wheat-Maize Rotation Using Remotely Sensed Time-Series Data. Remote Sens., 10.","DOI":"10.3390\/rs10081203"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Khan, A., Hansen, M.C., Potapov, P.V., Adusei, B., Pickens, A., Krylov, A., and Stehman, S.V. (2018). Evaluating Landsat and RapidEye Data for Winter Wheat Mapping and Area Estimation in Punjab, Pakistan. Remote Sens., 10.","DOI":"10.3390\/rs10040489"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Jiang, M., Xin, L.J., Li, X.B., Tan, M.H., and Wang, R.J. (2019). Decreasing Rice Cropping Intensity in Southern China from 1990 to 2015. Remote Sens., 11.","DOI":"10.3390\/rs11010035"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Dong, Q., Chen, X.H., Chen, J., Zhang, C.S., Liu, L.C., Cao, X., Zang, Y.Z., Zhu, X.F., and Cui, X.H. (2020). Mapping Winter Wheat in North China Using Sentinel 2A\/B Data: A Method Based on Phenology-Time Weighted Dynamic Time Warping. Remote Sens., 12.","DOI":"10.3390\/rs12081274"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"5969","DOI":"10.5194\/essd-13-5969-2021","article-title":"NESEA-Rice10: High-resolution annual paddy rice maps for Northeast and Southeast Asia from 2017 to 2019","volume":"13","author":"Han","year":"2021","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Chen, Y., Yu, P., Chen, Y., and Chen, Z. (2022). Spatiotemporal dynamics of rice\u2013crayfish field in Mid-China and its socioeconomic benefits on rural revitalisation. Appl. Geogr., 139.","DOI":"10.1016\/j.apgeog.2022.102636"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Frolking, S., Qiu, J., Boles, S., Xiao, X., Liu, J., Zhuang, Y., Li, C., and Qin, X. (2002). Combining remote sensing and ground census data to develop new maps of the distribution of rice agriculture in China. Glob. Biogeochem. Cycles, 16.","DOI":"10.1029\/2001GB001425"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"959","DOI":"10.1007\/s11284-013-1078-1","article-title":"Responses of a rice\u2013wheat rotation agroecosystem to experimental warming","volume":"28","author":"Cheng","year":"2013","journal-title":"Ecol. Res."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.rse.2016.02.028","article-title":"A meta-analysis of remote sensing research on supervised pixel-based land-cover image classification processes: General guidelines for practitioners and future research","volume":"177","author":"Khatami","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.rse.2017.03.047","article-title":"A multi-resolution approach to national-scale cultivated area estimation of soybean","volume":"195","author":"King","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.isprsjprs.2013.08.007","article-title":"Impact of feature selection on the accuracy and spatial uncertainty of per-field crop classification using support vector machines","volume":"85","author":"Michel","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"490","DOI":"10.1016\/j.rse.2017.06.033","article-title":"MODIS phenology-derived, multi-year distribution of conterminous US crop types","volume":"198","author":"Massey","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"407","DOI":"10.14358\/PERS.82.6.407","article-title":"An assessment of algorithmic parameters affecting image classification accuracy by random forests","volume":"82","author":"Shi","year":"2016","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Xu, J., Zhu, Y., Zhong, R., Lin, Z., Xu, J., Jiang, H., Huang, J., Li, H., and Lin, T. (2020). DeepCropMapping: A multi-temporal deep learning approach with improved spatial generalizability for dynamic corn and soybean mapping. Remote Sens. Environ., 247.","DOI":"10.1016\/j.rse.2020.111946"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.rse.2017.05.025","article-title":"Landsat-based classification in the cloud: An opportunity for a paradigm shift in land cover monitoring","volume":"202","author":"Azzari","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2141","DOI":"10.1080\/10106049.2019.1700556","article-title":"Crop classification in a heterogeneous agricultural environment using ensemble classifiers and single-date Sentinel-2A imagery","volume":"36","author":"Saini","year":"2021","journal-title":"Geocarto Int."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1080\/10095020.2020.1782776","article-title":"Crop type mapping using LiDAR, Sentinel-2 and aerial imagery with machine learning algorithms","volume":"24","author":"Prins","year":"2021","journal-title":"Geo-Spat. Inf. Sci."},{"key":"ref_25","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 T Geosci. Remote"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Zhang, L., Liu, Z., Ren, T.W., Liu, D.Y., Ma, Z., Tong, L., Zhang, C., Zhou, T.Y., Zhang, X.D., and Li, S.M. (2020). Identification of Seed Maize Fields with High Spatial Resolution and Multiple Spectral Remote Sensing Using Random Forest Classifier. Remote Sens., 12.","DOI":"10.3390\/rs12030362"},{"key":"ref_27","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_28","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.isprsjprs.2018.01.021","article-title":"Land cover mapping at very high resolution with rotation equivariant CNNs: Towards small yet accurate models","volume":"145","author":"Marcos","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_29","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_30","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_31","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2017, January 21\u201326). Xception: Deep learning with depthwise separable convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref_34","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 Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI 2015: 18th International Conference, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Dong, Z., Wang, G.J., Amankwah, S.O.Y., Wei, X.K., Hu, Y.F., and Feng, A.Q. (2021). Monitoring the summer flooding in the Poyang Lake area of China in 2020 based on Sentinel-1 data and multiple convolutional neural networks. Int. J. Appl. Earth Obs. Geoinf., 102.","DOI":"10.1016\/j.jag.2021.102400"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Fourure, D., Emonet, R., Fromont, E., Muselet, D., Tremeau, A., and Wolf, C. (2017). Residual conv-deconv grid network for semantic segmentation. arXiv.","DOI":"10.5244\/C.31.181"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_38","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":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_39","unstructured":"Sun, K., Zhao, Y., Jiang, B., Cheng, T., Xiao, B., Liu, D., Mu, Y., Wang, X., Liu, W., and Wang, J. (2019). High-resolution representations for labeling pixels and regions. arXiv."},{"key":"ref_40","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., and Polosukhin, I. (2017, January 4\u20139). Attention is all you need. Proceedings of the Advances in Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_41","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021, January 11\u201317). Swin transformer: Hierarchical vision transformer using shifted windows. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_43","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_44","doi-asserted-by":"crossref","unstructured":"Wang, X., Zhang, J.H., Xun, L., Wang, J.W., Wu, Z.J., Henchiri, M., Zhang, S.C., Zhang, S., Bai, Y., and Yang, S.S. (2022). Evaluating the Effectiveness of Machine Learning and Deep Learning Models Combined Time-Series Satellite Data for Multiple Crop Types Classification over a Large-Scale Region. Remote Sens., 14.","DOI":"10.3390\/rs14102341"},{"key":"ref_45","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_46","first-page":"32","article-title":"Climate change and its impact on water resources in the Huai River Basin","volume":"26","author":"Zuo","year":"2012","journal-title":"Bull. Chin. Acad. Sci."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"2841","DOI":"10.3390\/su7032841","article-title":"Detection and Modeling of Vegetation Phenology Spatiotemporal Characteristics in the Middle Part of the Huai River Region in China","volume":"7","author":"Xu","year":"2015","journal-title":"Sustainability"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Shorten, C., and Khoshgoftaar, T.M. (2019). A survey on Image Data Augmentation for Deep Learning. J. Big Data, 6.","DOI":"10.1186\/s40537-019-0197-0"},{"key":"ref_49","unstructured":"Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., and LeCun, Y. (2013). Overfeat: Integrated recognition, localization and detection using convolutional networks. arXiv."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Papandreou, G., Kokkinos, I., and Savalle, P.-A. (2015, January 7\u201312). Modeling local and global deformations in deep learning: Epitomic convolution, multiple instance learning, and sliding window detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298636"},{"key":"ref_51","unstructured":"Holschneider, M., Kronland-Martinet, R., Morlet, J., and Tchamitchian, P. (1990). Proceedings of the Wavelets: Time-Frequency Methods and Phase Space Proceedings of the International Conference."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Giusti, A., Cire\u015fan, D.C., Masci, J., Gambardella, L.M., and Schmidhuber, J. (2013, January 15\u201318). Fast image scanning with deep max-pooling convolutional neural networks. Proceedings of the 2013 IEEE International Conference on Image Processing, Melbourne, Australia.","DOI":"10.1109\/ICIP.2013.6738831"},{"key":"ref_53","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_54","unstructured":"Chen, L.-C., Papandreou, G., Schroff, F., and Adam, H. (2017). Rethinking atrous convolution for semantic image segmentation. arXiv."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Bai, H., Mao, H., and Nair, D. (2022, January 22\u201327). Dynamically pruning segformer for efficient semantic segmentation. Proceedings of the ICASSP 2022\u20142022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Singapore.","DOI":"10.1109\/ICASSP43922.2022.9747634"},{"key":"ref_56","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":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Zhao, H., Qi, X., Shen, X., Shi, J., and Jia, J. (2018, January 8\u201314). Icnet for real-time semantic segmentation on high-resolution images. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01219-9_25"},{"key":"ref_59","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_60","doi-asserted-by":"crossref","unstructured":"Wang, W., Xie, E., Li, X., Fan, D.-P., Song, K., Liang, D., Lu, T., Luo, P., and Shao, L. (2021, January 11\u201317). Pyramid vision transformer: A versatile backbone for dense prediction without convolutions. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.00061"},{"key":"ref_61","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_62","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.isprsjprs.2011.11.002","article-title":"An assessment of the effectiveness of a random forest classifier for land-cover classification","volume":"67","author":"Ghimire","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1080\/01431160412331269698","article-title":"Random forest classifier for remote sensing classification","volume":"26","author":"Pal","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.isprsjprs.2015.03.002","article-title":"Random Forest and Rotation Forest for fully polarized SAR image classification using polarimetric and spatial features","volume":"105","author":"Du","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_65","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Goyal, P., Girshick, R., He, K., and Doll\u00e1r, P. (2017, January 22\u201329). Focal loss for dense object detection. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Wang, G., Wu, M., Wei, X., and Song, H. (2020). Water identification from high-resolution remote sensing images based on multidimensional densely connected convolutional neural networks. Remote Sens., 12.","DOI":"10.3390\/rs12050795"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Elert, E. (2014). Rice by the numbers: A good grain. Nature, 514.","DOI":"10.1038\/514S50a"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Dang, B., and Li, Y.S. (2021). MSResNet: Multiscale Residual Network via Self-Supervised Learning for Water-Body Detection in Remote Sensing Imagery. Remote Sens., 13.","DOI":"10.3390\/rs13163122"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A.C., and Lo, W.-Y. (2023). Segment anything. arXiv.","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"ref_71","unstructured":"Takikawa, T., Acuna, D., Jampani, V., and Fidler, S. (November, January 27). Gated-scnn: Gated shape cnns for semantic segmentation. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_72","unstructured":"Li, X., Zhao, H., Han, L., Tong, Y., Tan, S., and Yang, K. (2020, January 7\u201312). Gated fully fusion for semantic segmentation. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/13\/3417\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:07:00Z","timestamp":1760126820000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/13\/3417"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,6]]},"references-count":72,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2023,7]]}},"alternative-id":["rs15133417"],"URL":"https:\/\/doi.org\/10.3390\/rs15133417","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,6]]}}}