{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T11:27:41Z","timestamp":1783164461678,"version":"3.54.6"},"reference-count":54,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,1,23]],"date-time":"2022-01-23T00:00:00Z","timestamp":1642896000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Currently, an increasing number of convolutional neural networks (CNNs) focus specifically on capturing contextual features (con. feat) to improve performance in semantic segmentation tasks. However, high-level con. feat are biased towards encoding features of large objects, disregard spatial details, and have a limited capacity to discriminate between easily confused classes (e.g., trees and grasses). As a result, we incorporate low-level features (low. feat) and class-specific discriminative features (dis. feat) to boost model performance further, with low. feat helping the model in recovering spatial information and dis. feat effectively reducing class confusion during segmentation. To this end, we propose a novel deep multi-feature learning framework for the semantic segmentation of VHR RSIs, dubbed MFNet. The proposed MFNet adopts a multi-feature learning mechanism to learn more complete features, including con. feat, low. feat, and dis. feat. More specifically, aside from a widely used context aggregation module for capturing con. feat, we additionally append two branches for learning low. feat and dis. feat. One focuses on learning low. feat at a shallow layer in the backbone network through local contrast processing, while the other groups con. feat and then optimizes each class individually to generate dis. feat with better inter-class discriminative capability. Extensive quantitative and qualitative evaluations demonstrate that the proposed MFNet outperforms most state-of-the-art models on the ISPRS Vaihingen and Potsdam datasets. In particular, thanks to the mechanism of multi-feature learning, our model achieves an overall accuracy score of 91.91% on the Potsdam test set with VGG16 as a backbone, performing favorably against advanced models with ResNet101.<\/jats:p>","DOI":"10.3390\/rs14030533","type":"journal-article","created":{"date-parts":[[2022,1,23]],"date-time":"2022-01-23T20:34:40Z","timestamp":1642970080000},"page":"533","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["Semantic Segmentation of Very-High-Resolution Remote Sensing Images via Deep Multi-Feature Learning"],"prefix":"10.3390","volume":"14","author":[{"given":"Yanzhou","family":"Su","sequence":"first","affiliation":[{"name":"School of Information and Communication Engineering, University of Electronic and Science Technology of China, Chengdu 611731, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian","family":"Cheng","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, University of Electronic and Science Technology of China, Chengdu 611731, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0895-4963","authenticated-orcid":false,"given":"Haiwei","family":"Bai","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, University of Electronic and Science Technology of China, Chengdu 611731, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haijun","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Changtao","family":"He","sequence":"additional","affiliation":[{"name":"Sichuan Jiuzhou Eletric Group Co., Ltd., Mianyang 621000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Chen, W., Jiang, Z., Wang, Z., Cui, K., and Qian, X. (2019, January 15\u201320). Collaborative global-local networks for memory-efficient segmentation of ultra-high resolution images. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00913"},{"key":"ref_2","unstructured":"Simonyan, K., and Zisserman, A. (2015, January 7\u20139). Very Deep Convolutional Networks for Large-Scale Image Recognition. Proceedings of the International Conference on Learning Representations, San Diego, CA, USA."},{"key":"ref_3","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 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_4","unstructured":"Chen, L., Papandreou, G., Schroff, F., and Adam, H. (2017). Rethinking atrous convolution for semantic image segmentation. arXiv."},{"key":"ref_5","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_6","doi-asserted-by":"crossref","unstructured":"Chen, L., 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_7","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.isprsjprs.2017.12.007","article-title":"Semantic labeling in very high resolution images via a self-cascaded convolutional neural network","volume":"145","author":"Liu","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1016\/j.isprsjprs.2021.05.004","article-title":"An attention-fused network for semantic segmentation of very-high-resolution remote sensing imagery","volume":"177","author":"Yang","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.isprsjprs.2017.08.011","article-title":"Contextually guided very-high-resolution imagery classification with semantic segments","volume":"132","author":"Zhao","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"474","DOI":"10.1109\/LGRS.2018.2795531","article-title":"Fully convolutional networks for semantic segmentation of very high resolution remotely sensed images combined with DSM","volume":"15","author":"Sun","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"5367","DOI":"10.1109\/TGRS.2020.2964675","article-title":"Semantic segmentation of large-size VHR remote sensing images using a two-stage multiscale training architecture","volume":"58","author":"Ding","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","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 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00326"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Huang, Z., Wang, X., Huang, L., Huang, C., Wei, Y., and Liu, W. (2019, January 27\u201328). Ccnet: Criss-cross attention for semantic segmentation. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Korea.","DOI":"10.1109\/ICCV.2019.00069"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Shi, H., Li, H., Wu, Q., and Song, Z. (2019, January 15\u201320). Scene parsing via integrated classification model and variance-based regularization. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00545"},{"key":"ref_15","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_16","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, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00813"},{"key":"ref_17","unstructured":"Yue, K., Sun, M., Yuan, Y., Zhou, F., Ding, E., and Xu, F. (2018, January 3\u20138). Compact generalized non-local network. Proceedings of the 32nd International Conference on Neural Information Processing Systems, Montr\u00e9al, QC, Canada."},{"key":"ref_18","unstructured":"Li, X., Zhang, L., You, A., Yang, M., Yang, K., and Tong, Y. (2019). Global Aggregation then Local Distribution in Fully Convolutional Networks. arXiv."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Luo, Z., Mishra, A., Achkar, A., Eichel, J., Li, S., and Jodoin, P. (2017, January 21\u201326). Non-local deep features for salient object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.698"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"582","DOI":"10.1109\/TCSVT.2020.2980853","article-title":"Edge-guided non-local fully convolutional network for salient object detection","volume":"31","author":"Tu","year":"2020","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Zhong, Z., Lin, Z., Bidart, R., Hu, X., Daya, I., Li, Z., Zheng, W., Li, J., and Wong, A. (2020, January 13\u201319). Squeeze-and-attention networks for semantic segmentation. Proceedings of the IEEE\/CVF Conference On Computer Vision And Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01308"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K. (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_23","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.isprsjprs.2020.01.013","article-title":"ResUNet-a: A deep learning framework for semantic segmentation of remotely sensed data","volume":"162","author":"Diakogiannis","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"905","DOI":"10.1109\/LGRS.2020.2988294","article-title":"SCAttNet: Semantic segmentation network with spatial and channel attention mechanism for high-resolution remote sensing images","volume":"18","author":"Li","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1109\/LGRS.2020.2983464","article-title":"PEGNet: Progressive edge guidance network for semantic segmentation of remote sensing images","volume":"18","author":"Pan","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_26","first-page":"91","article-title":"Faster r-cnn: Towards real-time object detection with region proposal networks","volume":"28","author":"Ren","year":"2015","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Mou, L., Hua, Y., and Zhu, X. (2019, January 16\u201317). A relation-augmented fully convolutional network for semantic segmentation in aerial scenes. Proceedings of the IEEE\/CVF Conference On Computer Vision And Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.01270"},{"key":"ref_28","first-page":"11418","article-title":"Gated fully fusion for semantic segmentation","volume":"34","author":"Li","year":"2020","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Chen, X., Han, Z., Liu, X., Li, Z., Fang, T., Huo, H., Li, Q., Zhu, M., Liu, M., and Yuan, H. (2021). Semantic boundary enhancement and position attention network with long-range dependency for semantic segmentation. Appl. Soft Comput., 109.","DOI":"10.1016\/j.asoc.2021.107511"},{"key":"ref_30","unstructured":"Yu, F., and Koltun, V. (2016, January 2\u20134). Multi-Scale Context Aggregation by Dilated Convolutions. Proceedings of the 4th International Conference on Learning Representations (ICLR 2016), San Juan, Puerto Rico."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Li, X., Li, X., Zhang, L., Cheng, G., Shi, J., Lin, Z., Tan, S., and Tong, Y. (2020, January 23\u201328). Improving semantic segmentation via decoupled body and edge supervision. Proceedings of the 16th European Conference, Glasgow, UK.","DOI":"10.1007\/978-3-030-58520-4_26"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.isprsjprs.2020.05.009","article-title":"UAVid: A semantic segmentation dataset for UAV imagery","volume":"165","author":"Lyu","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_33","unstructured":"(2021, December 15). ISPRS 2D Semantic Labeling Contest-Vaihingen. Available online: https:\/\/www2.isprs.org\/commissions\/comm2\/wg4\/benchmark\/2d-sem-label-vaihingen\/."},{"key":"ref_34","unstructured":"(2021, December 15). ISPRS 2D Semantic Labeling Contest-Potsdam. Available online: https:\/\/www2.isprs.org\/commissions\/comm2\/wg4\/benchmark\/2d-sem-label-potsdam\/."},{"key":"ref_35","first-page":"1","article-title":"Hybrid Multiple Attention Network for Semantic Segmentation in Aerial Images","volume":"60","author":"Niu","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_36","unstructured":"Xie, E., Wang, W., Yu, Z., An kumar, A., Alvarez, J., and Luo, P. (2021). SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers. arXiv."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"881","DOI":"10.1109\/TGRS.2016.2616585","article-title":"Dense semantic labeling of subdecimeter resolution images with convolutional neural networks","volume":"55","author":"Volpi","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","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_39","doi-asserted-by":"crossref","first-page":"7557","DOI":"10.1109\/TGRS.2020.2979552","article-title":"Relation matters: Relational context-aware fully convolutional network for semantic segmentation of high-resolution aerial images","volume":"58","author":"Mou","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"7503","DOI":"10.1109\/TGRS.2019.2913861","article-title":"Dynamic multicontext segmentation of remote sensing images based on convolutional networks","volume":"57","author":"Nogueira","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.isprsjprs.2017.11.011","article-title":"Beyond RGB: Very high resolution urban remote sensing with multimodal deep networks","volume":"140","author":"Audebert","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.isprsjprs.2017.11.009","article-title":"Classification with an edge: Improving semantic image segmentation with boundary detection","volume":"135","author":"Marmanis","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2019.07.007","article-title":"TreeUNet: Adaptive Tree convolutional neural networks for subdecimeter aerial image segmentation","volume":"156","author":"Yue","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Zhang, F., Chen, Y., Li, Z., Hong, Z., Liu, J., Ma, F., Han, J., and Ding, E. (2019, January 27\u201328). Acfnet: Attentional class feature network for semantic segmentation. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Korea.","DOI":"10.1109\/ICCV.2019.00690"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.isprsjprs.2018.06.005","article-title":"Developing a multi-filter convolutional neural network for semantic segmentation using high-resolution aerial imagery and LiDAR data","volume":"143","author":"Sun","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1016\/j.neucom.2018.11.051","article-title":"Problems of encoder-decoder frameworks for high-resolution remote sensing image segmentation: Structural stereotype and insufficient learning","volume":"330","author":"Sun","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"Segnet: A deep convolutional encoder-decoder architecture for image segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1109\/TITS.2017.2750080","article-title":"Erfnet: Efficient residual factorized convnet for real-time semantic segmentation","volume":"19","author":"Romera","year":"2017","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"3051","DOI":"10.1007\/s11263-021-01515-2","article-title":"Bisenet v2: Bilateral network with guided aggregation for real-time semantic segmentation","volume":"129","author":"Yu","year":"2021","journal-title":"Int. J. Comput. Vis."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.isprsjprs.2021.09.005","article-title":"ABCNet: Attentive bilateral contextual network for efficient semantic segmentation of Fine-Resolution remotely sensed imagery","volume":"181","author":"Li","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_52","first-page":"1","article-title":"Multiattention Network for Semantic Segmentation of Fine-Resolution Remote Sensing Images","volume":"60","author":"Li","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Wang, L., Li, R., Wang, D., Duan, C., Wang, T., and Meng, X. (2021). Transformer Meets Convolution: A Bilateral Awareness Network for Semantic Segmentation of Very Fine Resolution Urban Scene Images. Remote Sens., 13.","DOI":"10.3390\/rs13163065"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Li, R., Zheng, S., Zhang, C., Duan, C., and Wang, L. (2021). A2-FPN for Semantic Segmentation of Fine-Resolution Remotely Sensed Images. arXiv.","DOI":"10.1080\/01431161.2022.2030071"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/3\/533\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:06:12Z","timestamp":1760133972000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/3\/533"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,23]]},"references-count":54,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2022,2]]}},"alternative-id":["rs14030533"],"URL":"https:\/\/doi.org\/10.3390\/rs14030533","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,23]]}}}