{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:56:18Z","timestamp":1760147778798,"version":"build-2065373602"},"reference-count":49,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,2,28]],"date-time":"2023-02-28T00:00:00Z","timestamp":1677542400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science and Technology Project of National Bio Energy Co., Ltd.","award":["52789921001M"],"award-info":[{"award-number":["52789921001M"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>High-resolution remote sensing images (HRRSIs) cover a broad range of geographic regions and contain a wide variety of artificial objects and natural elements at various scales that comprise different image contexts. In semantic segmentation tasks based on deep convolutional neural networks (DCNNs), different resolution features are not equally effective for extracting ground objects with different scales. In this article, we propose a novel context-driven feature-focusing network (CFFNet) aimed at focusing on the multi-scale ground object in fused features of different resolutions. The CFFNet consists of three components: a depth-residual encoder, a context-driven feature-focusing (CFF) decoder, and a classifier. First, features with different resolutions are extracted using the depth-residual encoder to construct a feature pyramid. The multi-scale information in the fused features is then extracted using the feature-focusing (FF) module in the CFF decoder, followed by computing the focus weights of different scale features adaptively using the context-focusing (CF) module to obtain the weighted multi-scale fused feature representation. Finally, the final results are obtained using the classifier. The experiments are conducted on the public LoveDA and GID datasets. Quantitative and qualitative analyses of state-of-the-art (SOTA) segmentation benchmarks demonstrate the rationality and effectiveness of the proposed approach.<\/jats:p>","DOI":"10.3390\/rs15051348","type":"journal-article","created":{"date-parts":[[2023,2,28]],"date-time":"2023-02-28T02:28:09Z","timestamp":1677551289000},"page":"1348","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Context-Driven Feature-Focusing Network for Semantic Segmentation of High-Resolution Remote Sensing Images"],"prefix":"10.3390","volume":"15","author":[{"given":"Xiaowei","family":"Tan","sequence":"first","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8239-2268","authenticated-orcid":false,"given":"Zhifeng","family":"Xiao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanru","family":"Zhang","sequence":"additional","affiliation":[{"name":"National Bio Energy Co., Ltd., 3\/F, Taiping Financial Center, 16 Luomashi Street, Beijing 100052, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenjiang","family":"Wang","sequence":"additional","affiliation":[{"name":"National Bio Energy Co., Ltd., 3\/F, Taiping Financial Center, 16 Luomashi Street, Beijing 100052, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaole","family":"Qi","sequence":"additional","affiliation":[{"name":"National Bio Energy Co., Ltd., 3\/F, Taiping Financial Center, 16 Luomashi Street, Beijing 100052, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Deren","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,28]]},"reference":[{"key":"ref_1","first-page":"102706","article-title":"Enhanced contextual representation with deep neural networks for land cover classification based on remote sensing images","volume":"107","author":"Cheng","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Wang, L., Zhang, C., Li, R., Duan, C., Meng, X., and Atkinson, P.M. (2021). Scale-Aware Neural Network for Semantic Segmentation of Multi-Resolution Remote Sensing Images. Remote Sens., 13.","DOI":"10.3390\/rs13245015"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/10095020.2022.2053303","article-title":"Land cover classification from remote sensing images based on multi-scale fully convolutional network","volume":"25","author":"Li","year":"2022","journal-title":"Geo-Spat. Inf. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2818","DOI":"10.1109\/TGRS.2010.2041462","article-title":"A Novel Texture-Preceded Segmentation Algorithm for High-Resolution Imagery","volume":"48","author":"Li","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","first-page":"788","article-title":"A note on one class of perceptrons","volume":"25","author":"Vapnik","year":"1964","journal-title":"Autom. Remote Control"},{"key":"ref_6","first-page":"5","article-title":"Random Forests","volume":"45","author":"Pavlov","year":"1997","journal-title":"Karelian Cent. Russ. Acad. Sci. Petrozavodsk."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","article-title":"Distinctive Image Features from Scale-Invariant Keypoints","volume":"60","author":"Lowe","year":"2004","journal-title":"Int. J. Comput. Vis."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Teichmann, M., Araujo, A., Zhu, M., and Sim, J. (2018). Detect-to-Retrieve: Efficient Regional Aggregation for Image Search. CoRR.","DOI":"10.1109\/CVPR.2019.00525"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1016\/j.cviu.2007.09.014","article-title":"Speeded-Up Robust Features (SURF)","volume":"110","author":"Bay","year":"2008","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Li, R., Zheng, S., Duan, C., Yang, Y., and Wang, X. (2020). Classification of Hyperspectral Image Based on Double-Branch Dual-Attention Mechanism Network. Remote Sens., 12.","DOI":"10.20944\/preprints201912.0059.v2"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1109\/JSTARS.2019.2961634","article-title":"Multilabel Remote Sensing Image Retrieval Based on Fully Convolutional Network","volume":"13","author":"Shao","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"10990","DOI":"10.1109\/JSTARS.2021.3119654","article-title":"STransFuse: Fusing Swin Transformer and Convolutional Neural Network for Remote Sensing Image Semantic Segmentation","volume":"14","author":"Gao","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_13","first-page":"1","article-title":"Multiattention Network for Semantic Segmentation of Fine-Resolution Remote Sensing Images","volume":"60","author":"Li","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"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 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Lin, G., Milan, A., Shen, C., and Reid, I. (2017, January 21\u201326). RefineNet: Multi-path Refinement Networks for High-Resolution Semantic Segmentation. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.549"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1109\/LGRS.2020.2976551","article-title":"Scale Sensitive Neural Network for Road Segmentation in High-Resolution Remote Sensing Images","volume":"18","author":"Tan","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1131","DOI":"10.1080\/01431161.2022.2030071","article-title":"A2-FPN for semantic segmentation of fine-resolution remotely sensed images","volume":"43","author":"Li","year":"2022","journal-title":"Int. J. Remote Sens."},{"key":"ref_18","unstructured":"Ferrari, V., Hebert, M., Sminchisescu, C., and Weiss, Y. (, January 10\u201313). BiSeNet: Bilateral Segmentation Network for Real-Time Semantic Segmentation. Proceedings of the Computer Vision\u2013ECCV 2018, Munich, Germany."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, 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_20","doi-asserted-by":"crossref","unstructured":"Maier-Hein, G., Fritzsche, K.H., Deserno, G., Lehmann, T.M., Handels, H., and Tolxdorff, T. (2017, January 12\u201314). Invited Talk: U-Net Convolutional Networks for Biomedical Image Segmentation. Proceedings of the Bildverarbeitung f\u00fcr die Medizin 2017, Heidelberg, Germany.","DOI":"10.1007\/978-3-662-54345-0"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Ferrari, V., Hebert, M., Sminchisescu, C., and Weiss, Y. (2018, January 8\u201314). ICNet for Real-Time Semantic Segmentation on High-Resolution Images. Proceedings of the Computer Vision\u2014ECCV 2018, Munich, Germany.","DOI":"10.1007\/978-3-030-01225-0"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Huang, H., Lin, L., Tong, R., Hu, H., Zhang, Q., Iwamoto, Y., Han, X., Chen, Y.W., and Wu, J. (2020, January 4\u20138). UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation. Proceedings of the ICASSP 2020\u20142020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain.","DOI":"10.1109\/ICASSP40776.2020.9053405"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Stoyanov, D., Taylor, Z., Carneiro, G., Syeda-Mahmood, T., Martel, A., Maier-Hein, L., Tavares, J.M.R., Bradley, A., Papa, J.P., and Belagiannis, V. (2018). Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, Proceedings of the 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, 20 September 2018, Springer International Publishing.","DOI":"10.1007\/978-3-030-00889-5"},{"key":"ref_24","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":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J. (2017, January 21\u201326). Pyramid Scene Parsing Network. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Zhang, H., Dana, K., Shi, J., Zhang, Z., Wang, X., Tyagi, A., and Agrawal, A. (2018, January 18\u201322). Context Encoding for Semantic Segmentation. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00747"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2011","DOI":"10.1109\/TPAMI.2019.2913372","article-title":"Squeeze-and-Excitation Networks","volume":"42","author":"Hu","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_28","first-page":"28","article-title":"Spatial Transformer Networks","volume":"Volume 28","author":"Cortes","year":"2015","journal-title":"Advances in Neural Information Processing Systems, Proceedings of the Annual Conference on Neural Information Processing Systems 2015, Montreal, QC, Canada, 8\u201312 December 2015"},{"key":"ref_29","unstructured":"Guyon, I., Luxburg, U., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R. (2017). Advances In Neural Information Processing Systems 30, Proceedings of the Annual Conference on Neural Information Processing Systems (NIPS), Long Beach, CA, USA, 4\u20139 December 2017, Curran Associates, Inc."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Wu, W., Zhang, Y., Wang, D., and Leo, Y. (2020, January 7\u201312). SK-Net: Deep Learning on Point Cloud via End-to-End Discovery of Spatial Keypoints. Proceedings of the Thirty-Fourth AAAI Conference On Artificial Intelligence, The Thirty-Second Innovative Applications of Artificial Intelligence Conference and the Tenth AAAI Symposium on Educational Advances in Artificial Intelligence, ASSOC Advancement Artificial Intelligence, New York, NY, USA.","DOI":"10.1609\/aaai.v34i04.6113"},{"key":"ref_31","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 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., and Hu, Q. (2020, January 13\u201319). ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Ferrari, V., Hebert, M., Sminchisescu, C., and Weiss, Y. (2018, January 8\u201314). CBAM: Convolutional Block Attention Module. Proceedings of the Computer Vision\u2014ECCV 2018, Munich, Germany.","DOI":"10.1007\/978-3-030-01225-0"},{"key":"ref_34","first-page":"31","article-title":"A(2)-Nets: Double Attention Networks","volume":"Volume 31","author":"Bengio","year":"2018","journal-title":"Advances in Neural Information Processing Systems 31, (NIPS 2018), Proceedings of the 32nd Conference on Neural Information Processing Systems (NIPS), Montreal, QC, Canada, 2\u20138 December 2018"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-L\u00f3pez, C., and Fichtinger, G. (2018, January 16\u201320). Concurrent Spatial and Channel \u2018Squeeze & Excitation\u2019 in Fully Convolutional Networks. Proceedings of the Medical Image Computing and Computer Assisted Intervention\u2014MICCAI 2018, Granada, Spain.","DOI":"10.1007\/978-3-030-00937-3"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Wang, X., Girshick, R., Gupta, A., and He, K. (2018, January 18\u201323). Non-Local Neural Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00813"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Fu, J., Liu, J., Tian, H., Li, Y., Bao, Y., Fang, Z., and Lu, H. (2019, January 15\u201320). Dual Attention Network for Scene Segmentation. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00326"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Huang, Z., Wang, X., Huang, L., Huang, C., Wei, Y., and Liu, W. (2019, January 15\u201320). CCNet: Criss-Cross Attention for Semantic Segmentation. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), Long Beach, CA, USA.","DOI":"10.1109\/ICCV.2019.00069"},{"key":"ref_39","unstructured":"Lin, X., Guo, Y., and Wang, J. (2021). Global Correlation Network: End-to-End Joint Multi-Object Detection and Tracking. arXiv."},{"key":"ref_40","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_41","unstructured":"Ferrari, V., Hebert, M., Sminchisescu, C., and Weiss, Y. (2018, January 8\u201314). Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation. Proceedings of the Computer Vision\u2014ECCV 2018, Munich, Germany."},{"key":"ref_42","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_43","unstructured":"Nair, V., and Hinton, G.E. (2010, January 21\u201324). Rectified linear units improve Restricted Boltzmann machines. Proceedings of the 27th International Conference on Machine Learning (ICML-10), Haifa, Israel."},{"key":"ref_44","unstructured":"Wang, J., Zheng, Z., Ma, A., Lu, X., and Zhong, Y. (2021). LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation. arXiv."},{"key":"ref_45","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_46","unstructured":"Li, H., Xiong, P., An, J., and Wang, L. (2018). Pyramid Attention Network for Semantic Segmentation. arXiv."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Chaurasia, A., and Culurciello, E. (2017, January 10\u201313). LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation. Proceedings of the 2017 IEEE Visual Communications and Image Processing (VCIP), St. Petersburg, FL, USA.","DOI":"10.1109\/VCIP.2017.8305148"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Zheng, Z., Zhong, Y., Wang, J., and Ma, A. (2020, January 13\u201319). Foreground-Aware Relation Network for Geospatial Object Segmentation in High Spatial Resolution Remote Sensing Imagery. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00415"},{"key":"ref_49","first-page":"1","article-title":"FactSeg: Foreground Activation-Driven Small Object Semantic Segmentation in Large-Scale Remote Sensing Imagery","volume":"60","author":"Ma","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/5\/1348\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:43:48Z","timestamp":1760121828000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/5\/1348"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,28]]},"references-count":49,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["rs15051348"],"URL":"https:\/\/doi.org\/10.3390\/rs15051348","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2023,2,28]]}}}