{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T23:20:49Z","timestamp":1771024849852,"version":"3.50.1"},"reference-count":74,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,2,20]],"date-time":"2023-02-20T00:00:00Z","timestamp":1676851200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41901376"],"award-info":[{"award-number":["41901376"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["KLIGIP-2022-B08"],"award-info":[{"award-number":["KLIGIP-2022-B08"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Open Research Project of The Hubei Key Laboratory of 465 Intelligent Geo-Information Processing","award":["41901376"],"award-info":[{"award-number":["41901376"]}]},{"name":"Open Research Project of The Hubei Key Laboratory of 465 Intelligent Geo-Information Processing","award":["KLIGIP-2022-B08"],"award-info":[{"award-number":["KLIGIP-2022-B08"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Timely and accurate acquisition of crop type information is significant for irrigation scheduling, yield estimation, harvesting arrangement, etc. The unmanned aerial vehicle (UAV) has emerged as an effective way to obtain high resolution remote sensing images for crop type mapping. Convolutional neural network (CNN)-based methods have been widely used to predict crop types according to UAV remote sensing imagery, which has excellent local feature extraction capabilities. However, its receptive field limits the capture of global contextual information. To solve this issue, this study introduced the self-attention-based transformer that obtained long-term feature dependencies of remote sensing imagery as supplementary to local details for accurate crop-type segmentation in UAV remote sensing imagery and proposed an end-to-end CNN\u2013transformer feature-fused network (CTFuseNet). The proposed CTFuseNet first provided a parallel structure of CNN and transformer branches in the encoder to extract both local and global semantic features from the imagery. A new feature-fusion module was designed to flexibly aggregate the multi-scale global and local features from the two branches. Finally, the FPNHead of feature pyramid network served as the decoder for the improved adaptation to the multi-scale fused features and output the crop-type segmentation results. Our comprehensive experiments indicated that the proposed CTFuseNet achieved a higher crop-type-segmentation accuracy, with a mean intersection over union of 85.33% and a pixel accuracy of 92.46% on the benchmark remote sensing dataset and outperformed the state-of-the-art networks, including U-Net, PSPNet, DeepLabV3+, DANet, OCRNet, SETR, and SegFormer. Therefore, the proposed CTFuseNet was beneficial for crop-type segmentation, revealing the advantage of fusing the features found by the CNN and the transformer. Further work is needed to promote accuracy and efficiency of this approach, as well as to assess the model transferability.<\/jats:p>","DOI":"10.3390\/rs15041151","type":"journal-article","created":{"date-parts":[[2023,2,20]],"date-time":"2023-02-20T03:56:07Z","timestamp":1676865367000},"page":"1151","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["CTFuseNet: A Multi-Scale CNN-Transformer Feature Fused Network for Crop Type Segmentation on UAV Remote Sensing Imagery"],"prefix":"10.3390","volume":"15","author":[{"given":"Jianjian","family":"Xiang","sequence":"first","affiliation":[{"name":"School of Computer Science, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Jia","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Du","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Science, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Qi","family":"Xiong","sequence":"additional","affiliation":[{"name":"School of Computer Science, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Chongjiu","family":"Deng","sequence":"additional","affiliation":[{"name":"School of Computer Science, China University of Geosciences, Wuhan 430074, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,20]]},"reference":[{"key":"ref_1","unstructured":"FAO (2017). The Future of Food and Agriculture\u2013Trends and Challenges. Annu. Rep., 296, 1\u2013180."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Yi, Z., Jia, L., and Chen, Q. (2020). Crop Classification Using Multi-Temporal Sentinel-2 Data in the Shiyang River Basin of China. Remote Sens., 12.","DOI":"10.5194\/egusphere-egu2020-20926"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1016\/j.biosystemseng.2012.08.009","article-title":"Twenty Five Years of Remote Sensing in Precision Agriculture: Key Advances and Remaining Knowledge Gaps","volume":"114","author":"Mulla","year":"2013","journal-title":"Biosyst. Eng."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Liu, J., Xiang, J., Jin, Y., Liu, R., Yan, J., and Wang, L. (2021). Boost Precision Agriculture with Unmanned Aerial Vehicle Remote Sensing and Edge Intelligence: A Survey. Remote Sens., 13.","DOI":"10.3390\/rs13214387"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"179","DOI":"10.5194\/isprs-annals-IV-2-W5-179-2019","article-title":"Detecting Rumex Obtusifolius Weed Plants In Grasslands from UAV RGB Imagery Using Deep Learning","volume":"IV-2\/W5","author":"Valente","year":"2019","journal-title":"ISPRS Ann. Photogramm. Remote. Sens. Spat. Inf. Sci."},{"key":"ref_6","first-page":"102608","article-title":"Prediction of Insect-Herbivory-Damage and Insect-Type Attack in Maize Plants Using Hyperspectral Data","volume":"105","author":"Furuya","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Abdulridha, J., Batuman, O., and Ampatzidis, Y. (2019). UAV-Based Remote Sensing Technique to Detect Citrus Canker Disease Utilizing Hyperspectral Imaging and Machine Learning. Remote Sens., 11.","DOI":"10.3390\/rs11111373"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"126030","DOI":"10.1016\/j.eja.2020.126030","article-title":"Deep Learning Techniques for Estimation of the Yield and Size of Citrus Fruits Using a UAV","volume":"115","author":"Egea","year":"2020","journal-title":"Eur. J. Agron."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"3295","DOI":"10.1109\/JSTARS.2019.2922469","article-title":"Crop Type Identification and Mapping Using Machine Learning Algorithms and Sentinel-2 Time Series Data","volume":"12","author":"Feng","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"4441","DOI":"10.1109\/JSTARS.2018.2870650","article-title":"Comparative Performance Evaluation of Pixel-Level and Decision-Level Data Fusion of Landsat 8 OLI, Landsat 7 ETM+ and Sentinel-2 MSI for Crop Ensemble Classification","volume":"11","author":"Useya","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"4244","DOI":"10.1109\/JSTARS.2018.2866407","article-title":"A Novel Phenology Based Feature Subset Selection Technique Using Random Forest for Multitemporal PolSAR Crop Classification","volume":"11","author":"Hariharan","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"111605","DOI":"10.1016\/j.rse.2019.111605","article-title":"A Robust Spectral-Spatial Approach to Identifying Heterogeneous Crops Using Remote Sensing Imagery with High Spectral and Spatial Resolutions","volume":"239","author":"Zhao","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_13","first-page":"102598","article-title":"DOCC: Deep One-Class Crop Classification via Positive and Unlabeled Learning for Multi-Modal Satellite Imagery","volume":"105","author":"Lei","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_14","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_15","doi-asserted-by":"crossref","unstructured":"Badrinarayanan, V., Kendall, A., and Cipolla, R. (2016). SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation. arXiv.","DOI":"10.1109\/TPAMI.2016.2644615"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. arXiv.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., and Yuille, A.L. (2017). DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs. arXiv.","DOI":"10.1109\/TPAMI.2017.2699184"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Yang, M.D., Tseng, H.H., Hsu, Y.C., and Tseng, W.C. (2020, January 10\u201313). Real-Time Crop Classification Using Edge Computing and Deep Learning. Proceedings of the 2020 IEEE 17th Annual Consumer Communications & Networking Conference (CCNC), Las Vegas, NV, USA.","DOI":"10.1109\/CCNC46108.2020.9045498"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1171","DOI":"10.1007\/s11119-020-09777-5","article-title":"Semantic Segmentation of Citrus-Orchard Using Deep Neural Networks and Multispectral UAV-based Imagery","volume":"22","author":"Osco","year":"2021","journal-title":"Precis. Agric."},{"key":"ref_20","unstructured":"Chen, L.C., Papandreou, G., Schroff, F., and Adam, H. (2017). Rethinking Atrous Convolution for Semantic Image Segmentation. arXiv."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Zheng, S., Lu, J., Zhao, H., Zhu, X., Luo, Z., Wang, Y., Fu, Y., Feng, J., Xiang, T., and Torr, P.H.S. (2021). Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers. arXiv.","DOI":"10.1109\/CVPR46437.2021.00681"},{"key":"ref_22","unstructured":"Luo, W., Li, Y., Urtasun, R., and Zemel, R. (2016). Advances in Neural Information Processing Systems, Proceedings of the Thirtieth Conference on Neural Information Processing Systems, Barcelona, Spain, 5\u201310 December 2016, Curran Associates, Inc."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/978-3-030-01234-2_1","article-title":"CBAM: Convolutional Block Attention Module","volume":"Volume 11211","author":"Ferrari","year":"2018","journal-title":"Computer Vision\u2014ECCV 2018"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Albanie, S., Sun, G., and Wu, E. (2019). Squeeze-and-Excitation Networks. arXiv.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Gao, Z., Xie, J., Wang, Q., and Li, P. (2019, January 15\u201320). Global Second-Order Pooling Convolutional Networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00314"},{"key":"ref_26","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. (2017). Attention Is All You Need. arXiv."},{"key":"ref_27","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2021). An Image Is Worth 16x16 Words: Transformers for Image Recognition at Scale. arXiv."},{"key":"ref_28","first-page":"12077","article-title":"SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers","volume":"Volume 34","author":"Xie","year":"2021","journal-title":"Advances in Neural Information Processing Systems, Proceedings of the Conference on Neural Information Processing Systems, Virtual, 6\u201314 December 2021"},{"key":"ref_29","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_30","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2022.3230846","article-title":"Swin Transformer Embedding UNet for Remote Sensing Image Semantic Segmentation","volume":"60","author":"He","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Xu, Z., Zhang, W., Zhang, T., and Li, J. (2021). HRCNet: High-Resolution Context Extraction Network for Semantic Segmentation of Remote Sensing Images. Remote Sens., 13.","DOI":"10.3390\/rs13122290"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Wang, H., Chen, X., Zhang, T., Xu, Z., and Li, J. (2022). CCTNet: Coupled CNN and Transformer Network for Crop Segmentation of Remote Sensing Images. Remote Sens., 14.","DOI":"10.3390\/rs14091956"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Guo, J., Han, K., Wu, H., Tang, Y., Chen, X., Wang, Y., and Xu, C. (2022, January 18\u201324). CMT: Convolutional Neural Networks Meet Vision Transformers. Proceedings of the 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01186"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"847","DOI":"10.1109\/JSTARS.2020.2971763","article-title":"A CNN-Transformer Hybrid Approach for Crop Classification Using Multitemporal Multisensor Images","volume":"13","author":"Li","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Li, Q., Chen, Y., and Zeng, Y. (2022). Transformer with Transfer CNN for Remote-Sensing-Image Object Detection. Remote Sens., 14.","DOI":"10.3390\/rs14040984"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Li, S., Guo, Q., and Li, A. (2022). Pan-Sharpening Based on CNN plus Pyramid Transformer by Using No-Reference Loss. Remote Sens., 14.","DOI":"10.3390\/rs14030624"},{"key":"ref_37","first-page":"4505405","article-title":"High Resolution SAR Image Classification Using Global-Local Network Structure Based on Vision Transformer and CNN","volume":"19","author":"Liu","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Huang, L., Wang, F., Zhang, Y., and Xu, Q. (2022). Fine-Grained Ship Classification by Combining CNN and Swin Transformer. Remote Sens., 14.","DOI":"10.3390\/rs14133087"},{"key":"ref_39","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_40","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_41","doi-asserted-by":"crossref","unstructured":"Xiao, T., Liu, Y., Zhou, B., Jiang, Y., and Sun, J. (2018, January 8\u201314). Unified Perceptual Parsing for Scene Understanding. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01228-1_26"},{"key":"ref_42","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Dollar, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature Pyramid Networks for Object Detection. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Kirillov, A., Girshick, R., He, K., and Dollar, P. (2019, January 16\u201317). Panoptic Feature Pyramid Networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00656"},{"key":"ref_45","unstructured":"Tianchi (2022, December 28). Barley Remote Sensing Dataset. Available online: https:\/\/tianchi.aliyun.com\/dataset\/74952."},{"key":"ref_46","unstructured":"Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., and Schiele, B. (July, January 26). The Cityscapes Dataset for Semantic Urban Scene Understanding. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Zhou, B., Zhao, H., Puig, X., Fidler, S., Barriuso, A., and Torralba, A. (2017, January 21\u201326). Scene Parsing through ADE20K Dataset. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.544"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Fu, J., Liu, J., Tian, H., Li, Y., Bao, Y., Fang, Z., and Lu, H. (2019). Dual Attention Network for Scene Segmentation. arXiv.","DOI":"10.1109\/CVPR.2019.00326"},{"key":"ref_49","unstructured":"Yuan, Y., Chen, X., and Wang, J. (2020). Lecture Notes in Computer Science, Proceedings of the 16th European Conference Computer Vision (ECCV 2020), Glasgow, UK, 23\u201328 August 2020, Springer International Publishing."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Li, J., Shen, Y., and Yang, C. (2021). An Adversarial Generative Network for Crop Classification from Remote Sensing Timeseries Images. Remote Sens., 13.","DOI":"10.3390\/rs13010065"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"112012","DOI":"10.1016\/j.rse.2020.112012","article-title":"WHU-Hi: UAV-borne Hyperspectral with High Spatial Resolution (H2) Benchmark Datasets and Classifier for Precise Crop Identification Based on Deep Convolutional Neural Network with CRF","volume":"250","author":"Zhong","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1080\/19479832.2020.1838629","article-title":"A Variational Pan-Sharpening Algorithm to Enhance the Spectral and Spatial Details","volume":"12","author":"Gogineni","year":"2021","journal-title":"Int. J. Image Data Fusion"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Qu, Y., Zhao, W., Yuan, Z., and Chen, J. (2020). Crop Mapping from Sentinel-1 Polarimetric Time-Series with a Deep Neural Network. Remote Sens., 12.","DOI":"10.3390\/rs12152493"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1080\/19479832.2021.2019133","article-title":"Fusion and Classification of Multi-Temporal SAR and Optical Imagery Using Convolutional Neural Network","volume":"13","author":"Shakya","year":"2022","journal-title":"Int. J. Image Data Fusion"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1007\/s11263-019-01228-7","article-title":"Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization","volume":"128","author":"Selvaraju","year":"2020","journal-title":"Int. J. Comput. Vis."},{"key":"ref_56","unstructured":"Gildenblat, J. (2022, September 29). PyTorch Library for CAM Methods, 2021. Available online: https:\/\/github.com\/jacobgil\/pytorch-grad-cam."},{"key":"ref_57","unstructured":"Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., and Zagoruyko, S. (2020). Lecture Notes in Computer Science, Proceedings of the 16th European Conference on Computer Vision (ECCV 2020), Glasgow, UK, 23\u201328 August 2020, Springer International Publishing."},{"key":"ref_58","unstructured":"Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A.L., and Zhou, Y. (2021). TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation. arXiv."},{"key":"ref_59","first-page":"3965","article-title":"CoAtNet: Marrying Convolution and Attention for All Data Sizes","volume":"Volume 34","author":"Dai","year":"2021","journal-title":"Advances in Neural Information Processing Systems, Proceedings of the Conference on Neural Information Processing Systems, Online Event, 6\u201314 December 2021"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Wu, H., Xiao, B., Codella, N., Liu, M., Dai, X., Yuan, L., and Zhang, L. (2021, January 11\u201317). CvT: Introducing Convolutions to Vision Transformers. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.00009"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Peng, Z., Huang, W., Gu, S., Xie, L., Wang, Y., Jiao, J., and Ye, Q. (2021, January 11\u201317). Conformer: Local Features Coupling Global Representations for Visual Recognition. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.00042"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1007\/BF02295996","article-title":"Note on the sampling error of the difference between correlated proportions or percentages","volume":"12","author":"McNemar","year":"1947","journal-title":"Psychometrika"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"627","DOI":"10.14358\/PERS.70.5.627","article-title":"Thematic Map Comparison","volume":"70","author":"Foody","year":"2004","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Cris\u00f3stomo de Castro Filho, H., Ab\u00edlio de Carvalho J\u00fanior, O., Ferreira de Carvalho, O.L., Pozzobon de Bem, P., dos Santos de Moura, R., Olino de Albuquerque, A., Rosa Silva, C., Guimar\u00e3es Ferreira, P.H., Fontes Guimar\u00e3es, R., and Trancoso Gomes, R.A. (2020). Rice Crop Detection Using LSTM, Bi-LSTM, and Machine Learning Models from Sentinel-1 Time Series. Remote Sens., 12.","DOI":"10.3390\/rs12162655"},{"key":"ref_65","unstructured":"Greenwood, P.E., and Nikulin, M.S. (1996). A Guide to Chi-Squared Testing, John Wiley & Sons."},{"key":"ref_66","unstructured":"Seabold, S., and Perktold, J. (\u20133, January 28). statsmodels: Econometric and statistical modeling with python. Proceedings of the 9th Python in Science Conference, Austin, TX, USA."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"10468","DOI":"10.1109\/JSTARS.2021.3119001","article-title":"ReSFlow: A Remote Sensing Imagery Data-Flow for Improved Model Generalization","volume":"14","author":"Lunga","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"111322","DOI":"10.1016\/j.rse.2019.111322","article-title":"Land-cover classification with high-resolution remote sensing images using transferable deep models","volume":"237","author":"Tong","year":"2020","journal-title":"Remote. Sens. Environ."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"112265","DOI":"10.1016\/j.rse.2020.112265","article-title":"An automated, generalized, deep-learning-based method for delineating the calving fronts of Greenland glaciers from multi-sensor remote sensing imagery","volume":"254","author":"Zhang","year":"2021","journal-title":"Remote. Sens. Environ."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Qin, R., and Liu, T. (2022). A Review of Landcover Classification with Very-High Resolution Remotely Sensed Optical Images-Analysis Unit, Model Scalability and Transferability. Remote. Sens., 14.","DOI":"10.3390\/rs14030646"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Xiong, Y., Guo, S., Chen, J., Deng, X., Sun, L., Zheng, X., and Xu, W. (2020). Improved SRGAN for Remote Sensing Image Super-Resolution Across Locations and Sensors. Remote. Sens., 12.","DOI":"10.3390\/rs12081263"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Fu, J., Liu, J., Tian, H., Li, Y., Bao, Y., Fang, Z., and Lu, H. (2019, January 16\u201320). Dual Attention Network for Scene Segmentation. Proceedings of the 2019 IEEE\/CVF Conference On Computer Vision And Pattern Recognition (CVPR 2019), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00326"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"5349","DOI":"10.1109\/TNNLS.2020.2966319","article-title":"Why ResNet Works? Residuals Generalize","volume":"31","author":"He","year":"2020","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1016\/j.isprsjprs.2021.03.016","article-title":"A Global Context-aware and Batch-independent Network for road extraction from VHR satellite imagery","volume":"175","author":"Zhu","year":"2021","journal-title":"ISPRS J. Photogramm. Remote. Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/4\/1151\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:37:02Z","timestamp":1760121422000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/4\/1151"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,20]]},"references-count":74,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["rs15041151"],"URL":"https:\/\/doi.org\/10.3390\/rs15041151","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,20]]}}}