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Due to the spectral similarity between different crops, the influence of image resolution, the boundary blur and spatial inconsistency that often occur in remotely sensed crop mapping, remotely sensed crop mapping still faces great challenges. In this article, we propose to extend a neighborhood window centered on the target pixel to enhance the receptive field of our model and extract the spatial and spectral features of different neighborhood sizes through a multiscale network. In addition, we also designed a coordinate convolutional module and a convolutional block attention module to further enhance the spatial information and spectral features in the neighborhoods. Our experimental results show that this method allowed us to obtain accuracy scores of 0.9481, 0.9115, 0.9307 and 0.8729 for OA, kappa coefficient, F1 score and IOU, respectively, which were better than those obtained using other methods (Resnet-18, MLP and RFC). The comparison of the experimental results obtained from different neighborhood window sizes shows that the spatial inconsistency and boundary blurring in crop mapping could be effectively reduced by extending the neighborhood windows. It was also shown in the ablation experiments that the coordinate convolutional and convolutional block attention modules played active roles in the network. Therefore, the method proposed in this article could provide reliable technical support for remotely sensed crop mapping.<\/jats:p>","DOI":"10.3390\/rs15010047","type":"journal-article","created":{"date-parts":[[2022,12,23]],"date-time":"2022-12-23T03:02:15Z","timestamp":1671764535000},"page":"47","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["A Remote Sensing Method for Crop Mapping Based on Multiscale Neighborhood Feature Extraction"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5867-5356","authenticated-orcid":false,"given":"Yongchuang","family":"Wu","sequence":"first","affiliation":[{"name":"School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China"}]},{"given":"Yanlan","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Anhui University, Hefei 230601, China"},{"name":"Information Materials and Intelligent Sensing Laboratory of Anhui Province, Hefei 230601, China"},{"name":"Anhui Engineering Research Center for Geographical Information Intelligent Technology, Hefei 230601, China"},{"name":"Engineering Center for Geographic Information of Anhui Province, Hefei 230601, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3594-7953","authenticated-orcid":false,"given":"Biao","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China"},{"name":"Anhui Engineering Research Center for Geographical Information Intelligent Technology, Hefei 230601, China"},{"name":"Engineering Center for Geographic Information of Anhui Province, Hefei 230601, China"}]},{"given":"Hui","family":"Yang","sequence":"additional","affiliation":[{"name":"Institutes of Physical Science and Information Technology, Anhui University, Hefei 230601, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,22]]},"reference":[{"key":"ref_1","first-page":"812","article-title":"Food Security: The Challenge of Feeding 9 Billion People","volume":"327","author":"Godfray","year":"2010","journal-title":"Science (1979)"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1038\/nature10452","article-title":"Solutions for a Cultivated Planet","volume":"478","author":"Foley","year":"2011","journal-title":"Nature"},{"key":"ref_3","unstructured":"(2010). Food Security, Farming, and Climate Change to 2050: Scenarios, Results, Policy Options, International Food Policy Research Institute."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/S0034-4257(01)00296-6","article-title":"A Comparison of Methods for Monitoring Multitemporal Vegetation Change Using Thematic Mapper Imagery","volume":"80","author":"Rogan","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1080\/10095020.2017.1325594","article-title":"Sustainable Development and Geospatial Information: A Strategic Framework for Integrating a Global Policy Agenda into National Geospatial Capabilities","volume":"20","author":"Scott","year":"2017","journal-title":"Geo-Spat. Inf. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/j.isprsjprs.2003.10.002","article-title":"Multi-Resolution, Object-Oriented Fuzzy Analysis of Remote Sensing Data for GIS-Ready Information","volume":"58","author":"Benz","year":"2004","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Luo, C., Qi, B., Liu, H., Guo, D., Lu, L., Fu, Q., and Shao, Y. (2021). Using Time Series Sentinel-1 Images for Object-Oriented Crop Classification in Google Earth Engine. Remote Sens., 13.","DOI":"10.3390\/rs13040561"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"158223","DOI":"10.1109\/ACCESS.2019.2950371","article-title":"Deep Learning for Automatic Outlining Agricultural Parcels: Exploiting the Land Parcel Identification System","volume":"7","year":"2019","journal-title":"IEEE Access"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.isprsjprs.2014.06.014","article-title":"Object-Oriented Crop Mapping and Monitoring Using Multi-Temporal Polarimetric RADARSAT-2 Data","volume":"96","author":"Jiao","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_10","unstructured":"Geirhos, R., Michaelis, C., Wichmann, F.A., Rubisch, P., Bethge, M., and Brendel, W. (2019, January 6\u20139). Imagenet-Trained CNNs Are Biased towards Texture; Increasing Shape Bias Improves Accuracy and Robustness. Proceedings of the 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"sainte Fare Garnot, V., Landrieu, L., Giordano, S., and Chehata, N. (2020, January 13\u201319). Satellite Image Time Series Classification with Pixel-Set Encoders and Temporal Self-Attention. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01234"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1053","DOI":"10.14358\/PERS.79.11.1053","article-title":"Parcel-Level Identification of Crop Types Using Different Classification Algorithms and Multi-Resolution Imagery in Southeastern Turkey","volume":"79","author":"Alganci","year":"2013","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_13","unstructured":"Garnot, V.S.F., and Landrieu, L. (2021, January 10\u201317). Panoptic Segmentation of Satellite Image Time Series with Convolutional Temporal Attention Networks. Proceedings of the IEEE International Conference on Computer Vision, Montreal, QC, Canada."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1080\/10106049.2011.562309","article-title":"Monitoring US Agriculture: The US Department of Agriculture, National Agricultural Statistics Service, Cropland Data Layer Program","volume":"26","author":"Boryan","year":"2011","journal-title":"Geocarto Int."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Fisette, T., Rollin, P., Aly, Z., Campbell, L., Daneshfar, B., Filyer, P., Smith, A., Davidson, A., Shang, J., and Jarvis, I. (2013). AAFC Annual Crop Inventory. 2013 Second International Conference on Agro-Geoinformatics (Agro-Geoinformatics), IEEE.","DOI":"10.1109\/Argo-Geoinformatics.2013.6621920"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"239","DOI":"10.5194\/isprsannals-I-4-239-2012","article-title":"The challenge of automated change detection: Developing a method for the updating of land parcels","volume":"I-4","author":"Matikainen","year":"2012","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci."},{"key":"ref_17","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_18","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.compag.2017.01.003","article-title":"Plant Classification with In-Field-Labeling for Crop\/Weed Discrimination Using Spectral Features and 3D Surface Features from a Multi-Wavelength Laser Line Profile System","volume":"134","author":"Strothmann","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Rufin, P., Frantz, D., Ernst, S., Rabe, A., Griffiths, P., \u00f6zdo\u011fan, M., and Hostert, P. (2019). Mapping Cropping Practices on a National Scale Using Intra-Annual Landsat Time Series Binning. Remote Sens., 11.","DOI":"10.3390\/rs11030232"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2227","DOI":"10.1080\/01431160701395203","article-title":"Crop Classification by Support Vector Machine with Intelligently Selected Training Data for an Operational Application","volume":"29","author":"Mathur","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1016\/j.compag.2010.12.012","article-title":"Evaluating High Resolution SPOT 5 Satellite Imagery for Crop Identification","volume":"75","author":"Yang","year":"2011","journal-title":"Comput. Electron. Agric."},{"key":"ref_22","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_23","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.isprsjprs.2014.04.023","article-title":"Improved Maize Cultivated Area Estimation over a Large Scale Combining MODIS-EVI Time Series Data and Crop Phenological Information","volume":"94","author":"Zhang","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_24","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_25","doi-asserted-by":"crossref","first-page":"111673","DOI":"10.1016\/j.rse.2020.111673","article-title":"Introducing APiC for Regionalised Land Cover Mapping on the National Scale Using Sentinel-2A Imagery","volume":"240","author":"Preidl","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"430","DOI":"10.1016\/j.rse.2018.11.032","article-title":"Deep Learning Based Multi-Temporal Crop Classification","volume":"221","author":"Zhong","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_27","first-page":"122","article-title":"How Much Does Multi-Temporal Sentinel-2 Data Improve Crop Type Classification?","volume":"72","author":"Vuolo","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3162","DOI":"10.1080\/01431161.2019.1699973","article-title":"Learning Discriminative Spatiotemporal Features for Precise Crop Classification from Multi-Temporal Satellite Images","volume":"41","author":"Ji","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"111716","DOI":"10.1016\/j.rse.2020.111716","article-title":"Deep Learning in Environmental Remote Sensing: Achievements and Challenges","volume":"241","author":"Yuan","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_30","unstructured":"Luo, C., Meng, S., Hu, X., Wang, X., and Zhong, Y. (October, January 26). Cropnet: Deep Spatial-Temporal-Spectral Feature Learning Network for Crop Classification from Time-Series Multi-Spectral Images. Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS), Virtual."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"111946","DOI":"10.1016\/j.rse.2020.111946","article-title":"DeepCropMapping: A Multi-Temporal Deep Learning Approach with Improved Spatial Generalizability for Dynamic Corn and Soybean Mapping","volume":"247","author":"Xu","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/j.isprsjprs.2020.11.007","article-title":"Fully Convolutional Recurrent Networks for Multidate Crop Recognition from Multitemporal Image Sequences","volume":"171","author":"Feitosa","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_33","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_34","doi-asserted-by":"crossref","first-page":"112599","DOI":"10.1016\/j.rse.2021.112599","article-title":"Towards Interpreting Multi-Temporal Deep Learning Models in Crop Mapping","volume":"264","author":"Xu","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1016\/j.isprsjprs.2020.06.006","article-title":"Self-Attention for Raw Optical Satellite Time Series Classification","volume":"169","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_36","unstructured":"Ru\u00dfwurm, M., and K\u00f6rner, M. (2018, January 3\u20138). Convolutional LSTMs for Cloud-Robust Segmentation of Remote Sensing Imagery. Proceedings of the 32nd Conference on Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Ru\u00dfwurm, M., and K\u00f6rner, M. (2018). Multi-Temporal Land Cover Classification with Sequential Recurrent Encoders. ISPRS Int. J. Geoinf., 7.","DOI":"10.3390\/ijgi7040129"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Ren, J., Wang, R., Liu, G., Wang, Y., and Wu, W. (2021). An Svm-Based Nested Sliding Window Approach for Spectral\u2013Spatial Classification of Hyperspectral Images. Remote Sens., 13.","DOI":"10.3390\/rs13010114"},{"key":"ref_39","unstructured":"Liu, R., Lehman, J., Molino, P., Such, F.P., Frank, E., Sergeev, A., and Yosinski, J. (2018, January 3\u20138). An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution. Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_40","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 Computer Vision\u2013ECCV 2018, Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"106090","DOI":"10.1016\/j.compag.2021.106090","article-title":"A New Attention-Based CNN Approach for Crop Mapping Using Time Series Sentinel-2 Images","volume":"184","author":"Wang","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2013.08.023","article-title":"Efficient Corn and Soybean Mapping with Temporal Extendability: A Multi-Year Experiment Using Landsat Imagery","volume":"140","author":"Zhong","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_43","first-page":"1","article-title":"Mapping Paddy Rice Planting Area in Rice-Wetland Coexistent Areas through Analysis of Landsat 8 OLI and MODIS Images","volume":"46","author":"Zhou","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_44","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_45","doi-asserted-by":"crossref","unstructured":"Tu, Z., Talebi, H., Zhang, H., Yang, F., Milanfar, P., Bovik, A., and Li, Y. (2022, January 19\u201324). MAXIM: Multi-Axis MLP for Image Processing. Proceedings of the 2022 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00568"},{"key":"ref_46","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_47","doi-asserted-by":"crossref","first-page":"111017","DOI":"10.1016\/j.rse.2018.12.016","article-title":"Characterizing Land Cover\/Land Use from Multiple Years of Lansat and MODIS Time Series: A Novel Approach Using Land Surface Phenology Modeling and Random Forest Classifier","volume":"238","author":"Nguyen","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_48","first-page":"24261","article-title":"MLP-Mixer: An All-MLP Architecture for Vision","volume":"29","author":"Tolstikhin","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_49","unstructured":"Powers, D.M.W. (2007). Evaluation: From Precision, Recall and F-Factor to ROC, Informedness, Markedness & Correlation, School of Informatics and Engineering Flinders University."},{"key":"ref_50","unstructured":"Sasaki, Y. (2007). The Truth of the F-Measure. Teach. Tutor. Mater., 1\u20135. Available online: https:\/\/www.researchgate.net\/publication\/268185911."},{"key":"ref_51","unstructured":"Rahman, M.A., and Wang, Y. (2016, January 12\u201314). Optimizing Intersection-Over-Union in Deep. Proceedings of the International Symposium on Visual Computing, Las Vegas, NV, USA."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1177\/001316446002000104","article-title":"A Coefficient of Agreement for Nominal Scales","volume":"20","author":"Cohen","year":"1960","journal-title":"Educ. Psychol. Meas."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/1\/47\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:48:11Z","timestamp":1760147291000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/1\/47"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,22]]},"references-count":52,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["rs15010047"],"URL":"https:\/\/doi.org\/10.3390\/rs15010047","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,22]]}}}