{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,18]],"date-time":"2026-02-18T02:27:40Z","timestamp":1771381660602,"version":"3.50.1"},"reference-count":66,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2020,12,7]],"date-time":"2020-12-07T00:00:00Z","timestamp":1607299200000},"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>As one of the fundamental tasks in remote sensing (RS) image understanding, multi-label remote sensing image scene classification (MLRSSC) is attracting increasing research interest. Human beings can easily perform MLRSSC by examining the visual elements contained in the scene and the spatio-topological relationships of these visual elements. However, most of existing methods are limited by only perceiving visual elements but disregarding the spatio-topological relationships of visual elements. With this consideration, this paper proposes a novel deep learning-based MLRSSC framework by combining convolutional neural network (CNN) and graph neural network (GNN), which is termed the MLRSSC-CNN-GNN. Specifically, the CNN is employed to learn the perception ability of visual elements in the scene and generate the high-level appearance features. Based on the trained CNN, one scene graph for each scene is further constructed, where nodes of the graph are represented by superpixel regions of the scene. To fully mine the spatio-topological relationships of the scene graph, the multi-layer-integration graph attention network (GAT) model is proposed to address MLRSSC, where the GAT is one of the latest developments in GNN. Extensive experiments on two public MLRSSC datasets show that the proposed MLRSSC-CNN-GNN can obtain superior performance compared with the state-of-the-art methods.<\/jats:p>","DOI":"10.3390\/rs12234003","type":"journal-article","created":{"date-parts":[[2020,12,7]],"date-time":"2020-12-07T21:37:42Z","timestamp":1607377062000},"page":"4003","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":90,"title":["Multi-Label Remote Sensing Image Scene Classification by Combining a Convolutional Neural Network and a Graph Neural Network"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8203-1246","authenticated-orcid":false,"given":"Yansheng","family":"Li","sequence":"first","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2280-0135","authenticated-orcid":false,"given":"Ruixian","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9845-4251","authenticated-orcid":false,"given":"Yongjun","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China"}]},{"given":"Mi","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China"}]},{"given":"Ling","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,12,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4238","DOI":"10.1109\/TGRS.2015.2393857","article-title":"Effective and efficient midlevel visual elements-oriented land-use classification using VHR remote sensing images","volume":"53","author":"Cheng","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1109\/LGRS.2015.2503142","article-title":"Unsupervised multilayer feature learning for satellite image scene classification","volume":"13","author":"Li","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Li, Y., Zhang, Y., and Zhu, Z. (2020). Error-tolerant deep learning for remote sensing image scene classification. IEEE Trans. Cybern, in press.","DOI":"10.1109\/TCYB.2020.2989241"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.isprsjprs.2013.12.011","article-title":"Efficient, simultaneous detection of multi-class geospatial targets based on visual saliency modeling and discriminative learning of sparse coding","volume":"89","author":"Han","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"112045","DOI":"10.1016\/j.rse.2020.112045","article-title":"Accurate cloud detection in high-resolution remote sensing imagery by weakly supervised deep learning","volume":"250","author":"Li","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"7339","DOI":"10.1109\/TGRS.2019.2912985","article-title":"Scene context-driven vehicle detection in high-resolution aerial images","volume":"57","author":"Tao","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1016\/j.isprsjprs.2018.09.014","article-title":"Deep networks under scene-level supervision for multi-class geospatial object detection from remote sensing images","volume":"146","author":"Li","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"6521","DOI":"10.1109\/TGRS.2018.2839705","article-title":"Learning source-invariant deep hashing convolutional neural networks for cross-source remote sensing image retrieval","volume":"56","author":"Li","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"950","DOI":"10.1109\/TGRS.2017.2756911","article-title":"Large-scale remote sensing image retrieval by deep hashing neural networks","volume":"56","author":"Li","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.inffus.2020.10.008","article-title":"Image retrieval from remote sensing big data: A survey","volume":"67","author":"Li","year":"2021","journal-title":"Inf. Fusion."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Jian, L., Gao, F., Ren, P., Song, Y., and Luo, S. (2018). A noise-resilient online learning algorithm for scene classification. Remote Sens., 10.","DOI":"10.3390\/rs10111836"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zhang, W., Tang, P., and Zhao, L. (2019). Remote sensing image scene classification using CNN-CapsNet. Remote Sens., 11.","DOI":"10.3390\/rs11050494"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1109\/TGRS.2019.2931801","article-title":"Remote sensing scene classification by gated bidirectional network","volume":"58","author":"Sun","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3735","DOI":"10.1109\/JSTARS.2020.3005403","article-title":"Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities","volume":"13","author":"Cheng","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1285","DOI":"10.1109\/LGRS.2012.2237502","article-title":"Semantic Annotation of High-Resolution Remote Sensing Images via Gaussian Process Multi-Instance Multilabel Learning","volume":"10","author":"Chen","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Han, X.-H., and Chen, Y. (2017). Generalized aggregation of sparse coded multi-spectra for satellite scene classification. ISPRS Int. J. Geo-Inf., 6.","DOI":"10.20944\/preprints201705.0214.v1"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1016\/j.patcog.2016.07.001","article-title":"Towards better exploiting convolutional neural networks for remote sensing scene classification","volume":"61","author":"Nogueira","year":"2017","journal-title":"Pattern Recognit."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1144","DOI":"10.1109\/TGRS.2017.2760909","article-title":"Multilabel Remote Sensing Image Retrieval Using a Semisupervised Graph-Theoretic Method","volume":"56","author":"Chaudhuri","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Tan, Q., Liu, Y., Chen, X., and Yu, G. (2017). Multi-label classification based on low rank representation for image annotation. Remote Sens., 9.","DOI":"10.3390\/rs9020109"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","article-title":"The pascal visual object classes (VOC) challenge","volume":"88","author":"Everingham","year":"2010","journal-title":"Int. J. Comput. Vis."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., and Zitnick, C.L. (2014, January 6\u201312). Microsoft COCO: Common objects in context. Proceedings of the 2014 European Conference on Computer Vision (ECCV), Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1007\/s11263-016-0981-7","article-title":"Visual genome: Connecting language and vision using crowdsourced dense image annotations","volume":"123","author":"Krishna","year":"2017","journal-title":"Int. J. Comput. Vis."},{"key":"ref_23","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_24","doi-asserted-by":"crossref","unstructured":"Xie, S., Girshick, R., Dollar, P., Tu, Z., and He, K. (2017, January 21\u201326). Aggregated residual transformations for deep neural networks. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.634"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhou, X., Lin, M., and Sun, J. (2018, January 18\u201323). ShuffleNet: An extremely efficient convolutional neural network for mobile devices. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00716"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Yang, H., Zhou, J.T., Zhang, Y., Gao, B.-B., Wu, J., and Cai, J. (2016, January 27\u201330). Exploit bounding box annotations for multi-label object recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.37"},{"key":"ref_27","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_28","doi-asserted-by":"crossref","unstructured":"Wang, J., Yang, Y., Mao, J., Huang, Z., Huang, C., and Xu, W. (2016, January 27\u201330). CNN-RNN: A unified framework for multi-label image classification. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.251"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2801","DOI":"10.1109\/TMM.2018.2812605","article-title":"Multilabel image classification with regional latent semantic dependencies","volume":"20","author":"Zhang","year":"2018","journal-title":"IEEE Trans. Multimedia"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MGRS.2017.2762307","article-title":"Deep learning in remote sensing: A comprehensive review and list of resources","volume":"5","author":"Zhu","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"8555","DOI":"10.1109\/TGRS.2020.2988782","article-title":"RSVQA: Visual question answering for remote sensing data","volume":"58","author":"Lobry","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1031","DOI":"10.1109\/LGRS.2019.2893306","article-title":"Deep learning for multilabel land cover scene categorization using data augmentation","volume":"16","author":"Stivaktakis","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"694","DOI":"10.1109\/LGRS.2017.2671922","article-title":"A deep learning approach to UAV image multilabeling","volume":"14","author":"Zeggada","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1016\/j.isprsjprs.2019.01.015","article-title":"Recurrently exploring class-wise attention in a hybrid convolutional and bidirectional LSTM network for multi-label aerial image classification","volume":"149","author":"Hua","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_35","unstructured":"Lee, J., Lee, I., and Kang, J. (2019, January 9\u201315). Self-attention graph pooling. Proceedings of the 2019 International Conference on Machine Learning (ICML), Long Beach, CA, USA."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"884","DOI":"10.1109\/JSTSP.2017.2726981","article-title":"Robust spatial filtering with graph convolutional neural networks","volume":"11","author":"Such","year":"2017","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Zhang, M., Cui, Z., Neumann, M., and Chen, Y. (2018, January 2\u20137). An end-to-end deep learning architecture for graph classification. Proceedings of the 2018 AAAI Conference on Artificial Intelligence (AAAI), New Orleans, LA, USA.","DOI":"10.1609\/aaai.v32i1.11782"},{"key":"ref_38","unstructured":"Kipf, T.N., and Welling, M. (2017, January 24\u201326). Semi-supervised classification with graph convolutional networks. Proceedings of the 2017 International Conference on Learning Representations (ICLR), Toulon, France."},{"key":"ref_39","unstructured":"Li, Y., Zemel, R., Brockschmidt, M., and Tarlow, D. (2016, January 2\u20134). Gated graph sequence neural networks. Proceedings of the 2014 International Conference on Learning Representations (ICLR), San Juan, Puerto Rico."},{"key":"ref_40","unstructured":"Veli\u010dkovi\u0107, P., Cucurull, G., Casanova, A., Romero, A., Li\u00f2, P., and Bengio, Y. (May, January 30). Graph attention networks. Proceedings of the 2018 International Conference on Learning Representations (ICLR), Vancouver, BC, Canada."},{"key":"ref_41","unstructured":"Gilmer, J., Schoenholz, S.S., Riley, P.F., Vinyals, O., and Dahl, G.E. (2017, January 6\u201311). Neural message passing for quantum chemistry. Proceedings of the 2017 International Conference on Machine Learning (ICML), Sydney, Australia."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Chen, T., Xu, M., Hui, X., Wu, H., and Lin, L. (November, January 27). Learning semantic-specific graph representation for multi-label image recognition. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Korea.","DOI":"10.1109\/ICCV.2019.00061"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Chen, Z.-M., Wei, X.-S., Wang, P., and Guo, Y. (2019, January 15\u201320). Multi-label image recognition with graph convolutional networks. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00532"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1757","DOI":"10.1016\/j.patcog.2004.03.009","article-title":"Learning multi-label scene classification","volume":"37","author":"Boutell","year":"2004","journal-title":"Pattern Recognit."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"2473","DOI":"10.1109\/JSTARS.2018.2832985","article-title":"A novel system for content-based retrieval of single and multi-label high-dimensional remote sensing images","volume":"11","author":"Dai","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Sumbul, G., and Demir, B. (August, January 28). A novel multi-attention driven system for multi-label remote sensing image classification. Proceedings of the 2019 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8898188"},{"key":"ref_47","unstructured":"Senge, R., del Coz, J.J., and H\u00fcllermeier, E. (2012, January 1\u20133). On the problem of error propagation in classifier chains for multi-label classification. Proceedings of the 36th Annual Conference of the German Classification Society on Data Analysis, Machine Learning and Knowledge Discovery, Hildesheim, Germany."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"4558","DOI":"10.1109\/TGRS.2019.2963364","article-title":"Relation network for multilabel aerial image classification","volume":"58","author":"Hua","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_49","unstructured":"Kang, J., Fernandez-Beltran, R., Hong, D., Chanussot, J., and Plaza, A. (2020). Graph relation network: Modeling relations between scenes for multilabel remote-sensing image classification and retrieval. IEEE Trans. Geosci. Remote Sens., 1\u201315."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Wang, H., Xu, T., Liu, Q., Lian, D., Chen, E., Du, D., Wu, H., and Su, W. (2019, January 4\u20138). MCNE: An end-to-end framework for learning multiple conditional network representations of social network. Proceedings of the 2019 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Anchorage, AK, USA.","DOI":"10.1145\/3292500.3330931"},{"key":"ref_51","first-page":"346","article-title":"Session-based recommendation with graph neural networks","volume":"33","author":"Wu","year":"2019","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Nathani, D., Chauhan, J., Sharma, C., and Kaul, M. (August, January 28). Learning attention-based embeddings for relation prediction in knowledge graphs. Proceedings of the 2019 Annual Meeting of the Association for Computational Linguistics (ACL), Florence, Italy.","DOI":"10.18653\/v1\/P19-1466"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Yang, X., Tang, K., Zhang, H., and Cai, J. (2019, January 15\u201320). Auto-encoding scene graphs for image captioning. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.01094"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.cviu.2019.04.004","article-title":"Siamese graph convolutional network for content based remote sensing image retrieval","volume":"184","author":"Chaudhuri","year":"2019","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Gong, L., and Cheng, Q. (2019, January 15\u201320). Exploiting edge features for graph neural networks. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00943"},{"key":"ref_56","unstructured":"Yosinski, J., Clune, J., Bengio, Y., and Lipson, H. (2018, January 2\u20138). How transferable are features in deep neural networks?. Proceedings of the 2018 Conference on Neural Information Processing Systems (NIPS), Montreal, QC, Canada."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"2274","DOI":"10.1109\/TPAMI.2012.120","article-title":"SLIC superpixels compared to state-of-the-art superpixel methods","volume":"34","author":"Achanta","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_58","unstructured":"Ying, R., You, J., Morris, C., Ren, X., Hamilton, W.L., and Leskovec, J. (2018). Hierarchical graph representation learning with differentiable pooling. Adv. Neural Inf. Process. Syst."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Shao, Z., Yang, K., and Zhou, W. (2018). Performance evaluation of single-label and multi-label remote sensing image retrieval using a dense labeling dataset. Remote Sens., 10.","DOI":"10.3390\/rs10060964"},{"key":"ref_60","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_61","unstructured":"Wu, X.Z., and Zhou, Z.H. (2017, January 6\u201311). A unified view of multi-label performance measures. Proceedings of the 2017 International Conference on Machine Learning (ICML), Sydney, NSW, Australia."},{"key":"ref_62","unstructured":"Tsoumakas, G., and Vlahavas, I. (2007, January 17\u201321). Random k-labelsets: An ensemble method for multilabel classification. Proceedings of the 2007 European Conference on Machine Learning (ECML), Warsaw, Poland."},{"key":"ref_63","unstructured":"Simonyan, K., and Zisserman, A. (2015, January 7\u20139). Very deep convolutional networks for large-scale image recognition. Proceedings of the 2015 International Conference on Learning Representations (ICLR), San Diego, CA, USA."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","article-title":"ImageNet large scale visual recognition challenge","volume":"115","author":"Russakovsky","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"ref_65","first-page":"2121","article-title":"Adaptive subgradient methods for online learning and stochastic optimization","volume":"12","author":"Duchi","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.rse.2014.02.015","article-title":"Good practices for estimating area and assessing accuracy of land change","volume":"148","author":"Olofsson","year":"2014","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/23\/4003\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:41:59Z","timestamp":1760179319000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/23\/4003"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12,7]]},"references-count":66,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2020,12]]}},"alternative-id":["rs12234003"],"URL":"https:\/\/doi.org\/10.3390\/rs12234003","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,12,7]]}}}