{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T21:16:54Z","timestamp":1769635014361,"version":"3.49.0"},"reference-count":47,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2024,12,20]],"date-time":"2024-12-20T00:00:00Z","timestamp":1734652800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["XDA28070500"],"award-info":[{"award-number":["XDA28070500"]}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["20220201158GX"],"award-info":[{"award-number":["20220201158GX"]}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["OFSLRSS202214"],"award-info":[{"award-number":["OFSLRSS202214"]}]},{"name":"Jilin Scientific and Technological Development Program","award":["XDA28070500"],"award-info":[{"award-number":["XDA28070500"]}]},{"name":"Jilin Scientific and Technological Development Program","award":["20220201158GX"],"award-info":[{"award-number":["20220201158GX"]}]},{"name":"Jilin Scientific and Technological Development Program","award":["OFSLRSS202214"],"award-info":[{"award-number":["OFSLRSS202214"]}]},{"name":"Open Fund of State Key Laboratory of Remote Sensing Science","award":["XDA28070500"],"award-info":[{"award-number":["XDA28070500"]}]},{"name":"Open Fund of State Key Laboratory of Remote Sensing Science","award":["20220201158GX"],"award-info":[{"award-number":["20220201158GX"]}]},{"name":"Open Fund of State Key Laboratory of Remote Sensing Science","award":["OFSLRSS202214"],"award-info":[{"award-number":["OFSLRSS202214"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Accurate and timely crop distribution data are crucial for governments, in order to make related policies to ensure food security. However, agricultural ecosystems are spatially and temporally dynamic systems, which poses a great challenge for accurate crop mapping using fine spatial resolution (FSR) imagery. This research proposed a novel Tri-Dimensional Multi-head Self-Attention Network (TDMSANet) for accurate crop mapping from multitemporal fine-resolution remotely sensed images. Specifically, three sub-modules were designed to extract spectral, temporal, and spatial feature representations, respectively. All three sub-modules adopted a multi-head self-attention mechanism to assign higher weights to important features. In addition, the positional encoding was adopted by both temporal and spatial submodules to learn the sequence relationships between the features in a feature sequence. The proposed TDMSANet was evaluated on two sites utilizing FSR SAR (UAVSAR) and optical (Rapid Eye) images, respectively. The experimental results showed that TDMSANet consistently achieved significantly higher crop mapping accuracy compared to the benchmark models across both sites, with an average overall accuracy improvement of 1.40%, 3.35%, and 6.42% over CNN, Transformer, and LSTM, respectively. The ablation experiments further showed that the three sub-modules were all useful to the TDMSANet, and the Spatial Feature Extraction Module exerted larger impact than the remaining two sub-modules.<\/jats:p>","DOI":"10.3390\/rs16244755","type":"journal-article","created":{"date-parts":[[2024,12,23]],"date-time":"2024-12-23T09:13:38Z","timestamp":1734945218000},"page":"4755","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["TDMSANet: A Tri-Dimensional Multi-Head Self-Attention Network for Improved Crop Classification from Multitemporal Fine-Resolution Remotely Sensed Images"],"prefix":"10.3390","volume":"16","author":[{"given":"Jian","family":"Li","sequence":"first","affiliation":[{"name":"College of Information Technology, Jilin Agricultural University, Changchun 130118, China"}]},{"given":"Xuhui","family":"Tang","sequence":"additional","affiliation":[{"name":"College of Information Technology, Jilin Agricultural University, Changchun 130118, China"},{"name":"State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"}]},{"given":"Jian","family":"Lu","sequence":"additional","affiliation":[{"name":"College of Information Technology, Jilin Agricultural University, Changchun 130118, China"},{"name":"State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"}]},{"given":"Hongkun","family":"Fu","sequence":"additional","affiliation":[{"name":"College of Information Technology, Jilin Agricultural University, Changchun 130118, China"},{"name":"State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"}]},{"given":"Miao","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}]},{"given":"Jujian","family":"Huang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"},{"name":"Zhuhai Institute of Surveying and Mapping, Zhuhai 519000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5100-3584","authenticated-orcid":false,"given":"Ce","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Geographical Sciences, University of Bristol, Bristol BS8 1SS, UK"}]},{"given":"Huapeng","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China"},{"name":"State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1528","DOI":"10.1080\/17538947.2021.1950853","article-title":"A Scale Sequence Object-based Convolutional Neural Network (SS-OCNN) for crop classification from fine spatial resolution remotely sensed imagery","volume":"14","author":"Li","year":"2021","journal-title":"Int. J. Digit. Earth"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"921","DOI":"10.1111\/padr.12508","article-title":"Rethinking global food demand for 2050","volume":"48","author":"Falcon","year":"2022","journal-title":"Popul. Dev. Rev."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"113858","DOI":"10.1016\/j.rser.2023.113858","article-title":"Mapping smart farming: Addressing agricultural challenges in data-driven era","volume":"189","author":"Huo","year":"2024","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"114381","DOI":"10.1016\/j.rse.2024.114381","article-title":"An efficient and generalisable approach for mapping paddy rice fields based on their unique spectra during the transplanting period leveraging the CIE colour space","volume":"313","author":"Li","year":"2024","journal-title":"Remote Sens. Environ."},{"key":"ref_5","first-page":"231","article-title":"Remote sensing of crop health for food security in Africa: Potentials and constraints","volume":"8","author":"Mutanga","year":"2017","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2147","DOI":"10.1007\/s11442-022-2041-2","article-title":"Theoretical basis and technical path for the regional all-for-one customization model of black soil granary","volume":"32","author":"Liao","year":"2022","journal-title":"J. Geogr. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1038\/s41893-020-00656-5","article-title":"Proactive conservation to prevent habitat losses to agricultural expansion","volume":"4","author":"Williams","year":"2021","journal-title":"Nat. Sustain."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.cj.2019.06.005","article-title":"Deep neural network algorithm for estimating maize biomass based on simulated Sentinel 2A vegetation indices and leaf area index","volume":"8","author":"Jin","year":"2020","journal-title":"Crop J."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2017.02.001","article-title":"Winter wheat yield estimation based on multi-source medium resolution optical and radar imaging data and the AquaCrop model using the particle swarm optimization algorithm","volume":"126","author":"Jin","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Lu, J., Fu, H., Tang, X., Liu, Z., Huang, J., Zou, W., Chen, H., Sun, Y., Ning, X., and Li, J. (2024). GOA-optimized deep learning for soybean yield estimation using multi-source remote sensing data. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-57278-6"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Omia, E., Bae, H., Park, E., Kim, M.S., Baek, I., Kabenge, I., and Cho, B.-K. (2023). Remote sensing in field crop monitoring: A comprehensive review of sensor systems, data analyses and recent advances. Remote Sens., 15.","DOI":"10.3390\/rs15020354"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"106844","DOI":"10.1016\/j.compag.2022.106844","article-title":"A comprehensive review of remote sensing platforms, sensors, and applications in nut crops","volume":"197","author":"Jafarbiglu","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"8379391","DOI":"10.34133\/2021\/8379391","article-title":"Mapping crop phenology in near real-time using satellite remote sensing: Challenges and opportunities","volume":"2021","author":"Gao","year":"2021","journal-title":"J. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"eabe8628","DOI":"10.1126\/science.abe8628","article-title":"Using satellite imagery to understand and promote sustainable development","volume":"371","author":"Burke","year":"2021","journal-title":"Science"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1507","DOI":"10.1016\/j.cj.2022.07.005","article-title":"Temporal Sequence Object-based CNN (TS-OCNN) for crop classification from fine resolution remote sensing image time-series","volume":"10","author":"Li","year":"2022","journal-title":"Crop J."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"113679","DOI":"10.1016\/j.rse.2023.113679","article-title":"A novel Greenness and Water Content Composite Index (GWCCI) for soybean mapping from single remotely sensed multispectral images","volume":"295","author":"Chen","year":"2023","journal-title":"Remote Sens. Environ."},{"key":"ref_17","first-page":"102437","article-title":"Iterative Deep Learning (IDL) for agricultural landscape classification using fine spatial resolution remotely sensed imagery","volume":"102","author":"Li","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Fu, Y., Ye, Z., Deng, J., Zheng, X., Huang, Y., Yang, W., Wang, Y., and Wang, K. (2019). Finer resolution mapping of marine aquaculture areas using worldView-2 imagery and a hierarchical cascade convolutional neural network. Remote Sens., 11.","DOI":"10.3390\/rs11141678"},{"key":"ref_19","first-page":"101018","article-title":"Remotely sensed imagery and machine learning for mapping of sesame crop in the Brazilian Midwest","volume":"32","author":"Dallacort","year":"2023","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1949","DOI":"10.1007\/s12524-019-01041-2","article-title":"Evaluation of deep learning CNN model for land use land cover classification and crop identification using hyperspectral remote sensing images","volume":"47","author":"Bhosle","year":"2019","journal-title":"J. Indian Soc. Remote. Sens."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Lin, C., Jin, Z., Mulla, D., Ghosh, R., Guan, K., Kumar, V., and Cai, Y. (2021). Toward large-scale mapping of tree crops with high-resolution satellite imagery and deep learning algorithms: A case study of olive orchards in Morocco. Remote Sens., 13.","DOI":"10.3390\/rs13091740"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"837","DOI":"10.32604\/csse.2022.023016","article-title":"CNN based automated weed detection system using UAV imagery","volume":"42","author":"Haq","year":"2022","journal-title":"Comput. Syst. Sci. Eng."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhang, M., Lin, H., Wang, G., Sun, H., and Fu, J. (2018). Mapping paddy rice using a convolutional neural network (CNN) with Landsat 8 datasets in the Dongting Lake Area, China. Remote Sens., 10.","DOI":"10.3390\/rs10111840"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"104626","DOI":"10.1016\/j.micpro.2022.104626","article-title":"Optimal deep convolutional neural network based crop classification model on multispectral remote sensing images","volume":"94","author":"Chamundeeswari","year":"2022","journal-title":"Microprocess. Microsyst."},{"key":"ref_25","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_26","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_27","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_28","doi-asserted-by":"crossref","first-page":"746","DOI":"10.1002\/mp.13361","article-title":"Breast mass classification in sonography with transfer learning using a deep convolutional neural network and color conversion","volume":"46","author":"Byra","year":"2019","journal-title":"Med. Phys."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"934","DOI":"10.1109\/TIP.2020.3039574","article-title":"Ratio-and-scale-aware YOLO for pedestrian detection","volume":"30","author":"Hsu","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_30","unstructured":"Xie, Y., and Richmond, D. (2018, January 8\u201314). Pre-training on Grayscale ImageNet Improves Medical Image Classification. Proceedings of the Computer Vision\u2013ECCV 2018 Workshops, Munich, Germany. Proceedings, Part VI 15."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1460","DOI":"10.1016\/j.cj.2021.12.011","article-title":"Stacked spectral feature space patch: An advanced spectral representation for precise crop classification based on convolutional neural network","volume":"10","author":"Chen","year":"2022","journal-title":"Crop J."},{"key":"ref_32","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep residual learning for image recognition. Proceedings of the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_33","first-page":"5998","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_34","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., and Houlsby, N. (2020). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. arXiv."},{"key":"ref_35","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_36","doi-asserted-by":"crossref","first-page":"138869","DOI":"10.1016\/j.scitotenv.2020.138869","article-title":"Transfer Learning for Crop classification with Cropland Data Layer data (CDL) as training samples","volume":"733","author":"Hao","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Lark, T.J., Schelly, I.H., and Gibbs, H.K. (2021). Accuracy, bias, and improvements in mapping crops and cropland across the United States using the USDA cropland data layer. Remote Sens., 13.","DOI":"10.3390\/rs13050968"},{"key":"ref_38","unstructured":"Loshchilov, I., and Hutter, F. (2016, January 2\u20134). SGDR: Stochastic Gradient Descent with Warm Restarts. Proceedings of the International Conference on Learning Representations, San Juan, Puerto Rico."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"606","DOI":"10.1016\/j.rse.2006.10.010","article-title":"Comparative assessment of the measures of thematic classification accuracy","volume":"107","author":"Liu","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.rse.2018.06.034","article-title":"An object-based convolutional neural network (OCNN) for urban land use classification","volume":"216","author":"Zhang","year":"2018","journal-title":"Remote Sens. Environ."},{"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","unstructured":"Ji, S., Zhang, C., Xu, A., Shi, Y., and Duan, Y. (2018). 3D convolutional neural networks for crop classification with multi-temporal remote sensing images. Remote Sens., 10.","DOI":"10.3390\/rs10010075"},{"key":"ref_43","doi-asserted-by":"crossref","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 IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, HI, USA.","DOI":"10.1109\/IGARSS39084.2020.9324097"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1696","DOI":"10.1109\/TGRS.2019.2947708","article-title":"Spectral\u2013spatial\u2013temporal MAP-based sub-pixel mapping for land-cover change detection","volume":"58","author":"He","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Seydi, S.T., Amani, M., and Ghorbanian, A. (2022). A dual attention convolutional neural network for crop classification using time-series Sentinel-2 imagery. Remote Sens., 14.","DOI":"10.3390\/rs14030498"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"134677","DOI":"10.1109\/ACCESS.2019.2939152","article-title":"Temporal attention networks for multitemporal multisensor crop classification","volume":"7","author":"Li","year":"2019","journal-title":"Ieee Access"},{"key":"ref_47","unstructured":"Ramaswamy, H.G. (2020, January 1\u20135). Ablation-cam: Visual explanations for deep convolutional network via gradient-free localization. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, Snowmass Village, CO, USA."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/24\/4755\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:56:54Z","timestamp":1760115414000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/24\/4755"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,20]]},"references-count":47,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["rs16244755"],"URL":"https:\/\/doi.org\/10.3390\/rs16244755","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,20]]}}}