{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T22:35:50Z","timestamp":1765233350910,"version":"build-2065373602"},"reference-count":27,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2022,8,3]],"date-time":"2022-08-03T00:00:00Z","timestamp":1659484800000},"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>High-resolution remote sensing images with rich land surface structure can provide data support for accurately understanding more detailed change information of land cover and land use (LCLU) at different times. In this study, we present a novel scene change understanding framework for remote sensing which includes scene classification and change detection. To enhance the feature representation of images in scene classification, a robust label semantic relation learning (LSRL) network based on EfficientNet is presented for scene classification. It consists of a semantic relation learning module based on graph convolutional networks and a joint expression learning framework based on similarity. Since the bi-temporal remote sensing image pairs include spectral information in both temporal and spatial dimensions, land cover and land use change monitoring can be improved by using the relationship between different spatial and temporal locations. Therefore, a change detection method based on swin transformer blocks (STB-CD) is presented to obtain contextual relationships between targets. The experimental results on the LEVIR-CD, NWPU-RESISC45, and AID datasets demonstrate the superiority of LSRL and STB-CD over other state-of-the-art methods.<\/jats:p>","DOI":"10.3390\/rs14153709","type":"journal-article","created":{"date-parts":[[2022,8,3]],"date-time":"2022-08-03T23:33:01Z","timestamp":1659569581000},"page":"3709","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Scene Changes Understanding Framework Based on Graph Convolutional Networks and Swin Transformer Blocks for Monitoring LCLU Using High-Resolution Remote Sensing Images"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6773-471X","authenticated-orcid":false,"given":"Sihan","family":"Yang","sequence":"first","affiliation":[{"name":"School of Mechanical and Electrical Engineering, Chengdu University of Technology, Chengdu 610059, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0636-8343","authenticated-orcid":false,"given":"Fei","family":"Song","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611700, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0651-4278","authenticated-orcid":false,"given":"Gwanggil","family":"Jeon","sequence":"additional","affiliation":[{"name":"Department of Embedded Systems Engineering, Incheon National University, Incheon 22012, Korea"}]},{"given":"Rui","family":"Sun","sequence":"additional","affiliation":[{"name":"Unit 63636 of the Chinese People\u2019s Liberation Army, Lanzhou 735000, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/j.rse.2017.09.022","article-title":"Separate segmentation of multi-temporal high-resolution remote sensing images for object-based change detection in urban area","volume":"201","author":"Zhang","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"136763","DOI":"10.1016\/j.scitotenv.2020.136763","article-title":"Understanding the changes in spatial fairness of urban greenery using time-series remote sensing images: A case study of Guangdong-Hong Kong-Macao Greater Bay","volume":"715","author":"Yang","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Qiu, Y., Satoh, Y., Suzuki, R., Iwata, K., and Kataoka, H. (2020). Indoor scene change captioning based on multimodality data. Sensors, 20.","DOI":"10.3390\/s20174761"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"4743","DOI":"10.1109\/LRA.2020.3003290","article-title":"3d-aware scene change captioning from multiview images","volume":"5","author":"Qiu","year":"2020","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_5","unstructured":"Hall, D., Talbot, B., Bista, S.R., Zhang, H., Smith, R., Dayoub, F., and S\u00fcnderhauf, N. (2020). The robotic vision scene understanding challenge. arXiv."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"5148","DOI":"10.1109\/TGRS.2017.2702596","article-title":"Remote sensing scene classification by unsupervised representation learning","volume":"55","author":"Lu","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"77494","DOI":"10.1109\/ACCESS.2018.2883254","article-title":"Multi-scale feature based land cover change detection in mountainous terrain using multi-temporal and multi-sensor remote sensing images","volume":"6","author":"Song","year":"2018","journal-title":"IEEE Access"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"6508505","DOI":"10.1109\/LGRS.2022.3165885","article-title":"MSTDSNet-CD: Multiscale Swin Transformer and Deeply Supervised Network for Change Detection of the Fast-Growing Urban Regions","volume":"19","author":"Song","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1007\/BF00130487","article-title":"Color indexing","volume":"7","author":"Swain","year":"1991","journal-title":"Int. J. Comput. Vis."},{"key":"ref_10","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_11","unstructured":"Dalal, N., and Triggs, B. (2005, January 20\u201325). Histograms of oriented gradients for human detection. Proceedings of the 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR\u201905), San Diego, CA, USA."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Shen, J., Zhang, T., Wang, Y., Wang, R., Wang, Q., and Qi, M. (2021). A Dual-Model Architecture with Grouping-Attention-Fusion for Remote Sensing Scene Classification. Remote Sens., 13.","DOI":"10.3390\/rs13030433"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3677","DOI":"10.1109\/TGRS.2018.2886643","article-title":"Unsupervised deep change vector analysis for multiple-change detection in VHR images","volume":"57","author":"Saha","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"6524","DOI":"10.1109\/TGRS.2020.2977248","article-title":"Object-oriented key point vector distance for binary land cover change detection using VHR remote sensing images","volume":"58","author":"Lv","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","unstructured":"Daudt, R.C., Le Saux, B., and Boulch, A. (2018, January 7\u201310). Fully convolutional siamese networks for change detection. Proceedings of the 2018 25th IEEE International Conference on Image Processing (ICIP), Athens, Greece."},{"key":"ref_16","first-page":"5604816","article-title":"A deeply supervised attention metric-Based network and an open aerial image dataset for remote sensing change detection","volume":"60","author":"Shi","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021). Swin transformer: Hierarchical vision transformer using shifted windows. arXiv.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1865","DOI":"10.1109\/JPROC.2017.2675998","article-title":"Remote sensing image scene classification: Benchmark and state of the art","volume":"105","author":"Cheng","year":"2017","journal-title":"Proc. IEEE"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3965","DOI":"10.1109\/TGRS.2017.2685945","article-title":"AID: A benchmark data set for performance evaluation of aerial scene classification","volume":"55","author":"Xia","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Chen, H., and Shi, Z. (2020). A spatial-temporal attention-based method and a new dataset for remote sensing image change detection. Remote Sens., 12.","DOI":"10.3390\/rs12101662"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Zhang, J., Zhang, M., Shi, L., Yan, W., and Pan, B. (2019). A multi-scale approach for remote sensing scene classification based on feature maps selection and region representation. Remote Sens., 11.","DOI":"10.3390\/rs11212504"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1986","DOI":"10.1109\/JSTARS.2020.2988477","article-title":"Classification of high-spatial-resolution remote sensing scenes method using transfer learning and deep convolutional neural network","volume":"13","author":"Li","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1109\/LGRS.2020.2968550","article-title":"Self-attention-based deep feature fusion for remote sensing scene classification","volume":"18","author":"Cao","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"14078","DOI":"10.1109\/ACCESS.2021.3051085","article-title":"Classification of remote sensing images using EfficientNet-B3 CNN model with attention","volume":"9","author":"Alhichri","year":"2021","journal-title":"IEEE Access"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1080\/22797254.2020.1868273","article-title":"Remote sensing scene classification based on high-order graph convolutional network","volume":"54","author":"Gao","year":"2021","journal-title":"Eur. J. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"5501","DOI":"10.1109\/JSTARS.2021.3074508","article-title":"SEMSDNet: A multiscale dense network with attention for remote sensing scene classification","volume":"14","author":"Tian","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Shi, C., Zhang, X., and Wang, L. (2021). A Lightweight Convolutional Neural Network Based on Channel Multi-Group Fusion for Remote Sensing Scene Classification. Remote Sens., 14.","DOI":"10.3390\/rs14010009"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/15\/3709\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:01:40Z","timestamp":1760140900000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/15\/3709"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,3]]},"references-count":27,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2022,8]]}},"alternative-id":["rs14153709"],"URL":"https:\/\/doi.org\/10.3390\/rs14153709","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2022,8,3]]}}}