{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T22:32:55Z","timestamp":1776119575192,"version":"3.50.1"},"reference-count":61,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2021,12,3]],"date-time":"2021-12-03T00:00:00Z","timestamp":1638489600000},"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":["42101346"],"award-info":[{"award-number":["42101346"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2020M680109"],"award-info":[{"award-number":["2020M680109"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2042021kf0008"],"award-info":[{"award-number":["2042021kf0008"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Resources Science and Technology Project of Hubei Province","award":["ZRZY2021KJ01"],"award-info":[{"award-number":["ZRZY2021KJ01"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Semantic segmentation is a fundamental task in remote sensing image analysis (RSIA). Fully convolutional networks (FCNs) have achieved state-of-the-art performance in the task of semantic segmentation of natural scene images. However, due to distinctive differences between natural scene images and remotely-sensed (RS) images, FCN-based semantic segmentation methods from the field of computer vision cannot achieve promising performances on RS images without modifications. In previous work, we proposed an RS image semantic segmentation framework SDFCNv1, combined with a majority voting postprocessing method. Nevertheless, it still has some drawbacks, such as small receptive field and large number of parameters. In this paper, we propose an improved semantic segmentation framework SDFCNv2 based on SDFCNv1, to conduct optimal semantic segmentation on RS images. We first construct a novel FCN model with hybrid basic convolutional (HBC) blocks and spatial-channel-fusion squeeze-and-excitation (SCFSE) modules, which occupies a larger receptive field and fewer network model parameters. We also put forward a data augmentation method based on spectral-specific stochastic-gamma-transform-based (SSSGT-based) during the model training process to improve generalizability of our model. Besides, we design a mask-weighted voting decision fusion postprocessing algorithm for image segmentation on overlarge RS images. We conducted several comparative experiments on two public datasets and a real surveying and mapping dataset. Extensive experimental results demonstrate that compared with the SDFCNv1 framework, our SDFCNv2 framework can increase the mIoU metric by up to 5.22% while only using about half of parameters.<\/jats:p>","DOI":"10.3390\/rs13234902","type":"journal-article","created":{"date-parts":[[2021,12,6]],"date-time":"2021-12-06T03:10:38Z","timestamp":1638760238000},"page":"4902","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":57,"title":["SDFCNv2: An Improved FCN Framework for Remote Sensing Images Semantic Segmentation"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0733-9122","authenticated-orcid":false,"given":"Guanzhou","family":"Chen","sequence":"first","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7822-060X","authenticated-orcid":false,"given":"Xiaoliang","family":"Tan","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"}]},{"given":"Beibei","family":"Guo","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6279-9215","authenticated-orcid":false,"given":"Kun","family":"Zhu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"}]},{"given":"Puyun","family":"Liao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3915-0175","authenticated-orcid":false,"given":"Tong","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"}]},{"given":"Qing","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Geosciences, Yangtze University, Wuhan 430100, China"}]},{"given":"Xiaodong","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,3]]},"reference":[{"key":"ref_1","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_2","doi-asserted-by":"crossref","first-page":"2811","DOI":"10.1109\/TGRS.2017.2783902","article-title":"When deep learning meets metric learning: Remote sensing image scene classification via learning discriminative CNNs","volume":"56","author":"Cheng","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_3","first-page":"204","article-title":"Introducing eurosat: A novel dataset and deep learning benchmark for land use and land cover classification","volume":"2018","author":"Helber","year":"2018","journal-title":"Int. Geosci. Remote Sens. Symp."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"630","DOI":"10.1111\/geb.12022","article-title":"A high-resolution bioclimate map of the world: A unifying framework for global biodiversity research and monitoring","volume":"22","author":"Metzger","year":"2013","journal-title":"Glob. Ecol. Biogeogr."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.landurbplan.2012.08.001","article-title":"Mapping public and private spaces of urban agriculture in Chicago through the analysis of high-resolution aerial images in Google Earth","volume":"108","author":"Taylor","year":"2012","journal-title":"Landsc. Urban Plan."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"566","DOI":"10.1109\/JPROC.2012.2237076","article-title":"Advances in Very-High-Resolution Remote Sensing","volume":"101","author":"Benediktsson","year":"2013","journal-title":"Proc. IEEE"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"553","DOI":"10.1109\/LGRS.2019.2926402","article-title":"Convective Clouds Extraction From Himawari-8 Satellite Images Based on Double-Stream Fully Convolutional Networks","volume":"17","author":"Zhang","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1016\/j.neucom.2019.02.003","article-title":"Survey on semantic segmentation using deep learning techniques","volume":"338","author":"Lateef","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_9","first-page":"640","article-title":"Fully Convolutional Networks for Semantic Segmentation","volume":"39","author":"Long","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation, Springer. Miccai.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_11","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_12","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_13","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 30th IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.549"},{"key":"ref_14","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_15","unstructured":"Sun, K., Zhao, Y., Jiang, B., Cheng, T., Xiao, B., Liu, D., Mu, Y., Wang, X., Liu, W., and Wang, J. (2019). High-resolution representations for labeling pixels and regions. arXiv."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.isprsjprs.2019.04.015","article-title":"Deep learning in remote sensing applications: A meta-analysis and review","volume":"152","author":"Ma","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"111716","DOI":"10.1016\/j.rse.2020.111716","article-title":"Deep learning in environmental remote sensing: Achievements and challenges","volume":"241","author":"Yuan","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Talukdar, S., Singha, P., Mahato, S., Pal, S., Liou, Y.A., and Rahman, A. (2020). Land-use land-cover classification by machine learning classifiers for satellite observations\u2014A review. Remote Sens., 12.","DOI":"10.3390\/rs12071135"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Vali, A., Comai, S., and Matteucci, M. (2020). Deep learning for land use and land cover classification based on hyperspectral and multispectral earth observation data: A review. Remote Sens., 12.","DOI":"10.3390\/rs12152495"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Hoeser, T., Bachofer, F., and Kuenzer, C. (2020). Object detection and image segmentation with deep learning on earth observation data: A review-part II: Applications. Remote Sens., 12.","DOI":"10.3390\/rs12183053"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"8780","DOI":"10.1109\/TGRS.2020.2990640","article-title":"Unsupervised Deep Joint Segmentation of Multitemporal High-Resolution Images","volume":"58","author":"Saha","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","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_23","doi-asserted-by":"crossref","unstructured":"Hua, Y., Marcos, D., Mou, L., Zhu, X.X., and Tuia, D. (2021). Semantic Segmentation of Remote Sensing Images With Sparse Annotations. IEEE Geosci. Remote Sens. Lett.","DOI":"10.1109\/LGRS.2021.3051053"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1080\/2150704X.2016.1235299","article-title":"SatCNN: Satellite image dataset classification using agile convolutional neural networks","volume":"8","author":"Zhong","year":"2017","journal-title":"Remote Sens. Lett."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.neucom.2015.10.010","article-title":"Single satellite image dehazing via linear intensity transformation and local property analysis","volume":"175","author":"Ni","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Yu, H., Yang, W., Xia, G.S., and Liu, G. (2016). A Color-Texture-Structure Descriptor for High-Resolution Satellite Image Classification. Remote Sens., 8.","DOI":"10.3390\/rs8030259"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1016\/j.isprsjprs.2019.03.015","article-title":"A new fully convolutional neural network for semantic segmentation of polarimetric SAR imagery in complex land cover ecosystem","volume":"151","author":"Mohammadimanesh","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_28","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_29","first-page":"101897","article-title":"Using a U-net convolutional neural network to map woody vegetation extent from high resolution satellite imagery across Queensland, Australia","volume":"82","author":"Flood","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Miyoshi, G.T., Arruda, M.d.S., Osco, L.P., Junior, J.M., Gon\u00e7alves, D.N., Imai, N.N., Tommaselli, A.M.G., Honkavaara, E., and Gon\u00e7alves, W.N. (2020). A novel deep learning method to identify single tree species in UAV-based hyperspectral images. Remote Sens., 12.","DOI":"10.3390\/rs12081294"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1633","DOI":"10.1109\/JSTARS.2018.2810320","article-title":"Symmetrical Dense-Shortcut Deep Fully Convolutional Networks for Semantic Segmentation of Very-High-Resolution Remote Sensing Images","volume":"11","author":"Chen","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.ins.2020.05.062","article-title":"Global context based automatic road segmentation via dilated convolutional neural network","volume":"535","author":"Lan","year":"2020","journal-title":"Inf. Sci."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"293","DOI":"10.5194\/isprsannals-I-3-293-2012","article-title":"The isprs benchmark on urban object classification and 3D building reconstruction","volume":"1","author":"Rottensteiner","year":"2012","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Chen, L., Dou, X., Peng, J., Li, W., Sun, B., and Li, H. (2021). EFCNet: Ensemble Full Convolutional Network for Semantic Segmentation of High-Resolution Remote Sensing Images. IEEE Geosci. Remote Sens. Lett.","DOI":"10.1109\/LGRS.2021.3076093"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Huang, Z., Qi, H., Kang, C., Su, Y., and Liu, Y. (2020). An ensemble learning approach for urban land use mapping based on remote sensing imagery and social sensing data. Remote Sens., 12.","DOI":"10.3390\/rs12193254"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"10535","DOI":"10.1109\/JSTARS.2021.3094673","article-title":"Automatic Road Extraction from Remote Sensing Imagery Using Ensemble Learning and Postprocessing","volume":"14","author":"Li","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_37","unstructured":"Luo, W., Li, Y., Urtasun, R., and Zemel, R. (2019, January 5\u201310). Understanding the effective receptive field in deep convolutional neural networks. Proceedings of the 30th International Conference on Neural Information Processing Systems, Barcelona, Spain."},{"key":"ref_38","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (July, January 26). Rethinking the Inception Architecture for Computer Vision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). 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_40","unstructured":"Tolias, G., Sicre, R., and J\u00e9gou, H. (2015). Particular object retrieval with integral max-pooling of CNN activations. arXiv."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"833","DOI":"10.1007\/978-3-030-01234-2_49","article-title":"Encoder-decoder with atrous separable convolution for semantic image segmentation","volume":"11211","author":"Chen","year":"2018","journal-title":"Lect. Notes Comput. Sci."},{"key":"ref_42","unstructured":"Chen, L.C., Papandreou, G., Schroff, F., and Adam, H. (2017). Rethinking Atrous Convolution for Semantic Image Segmentation. arXiv."},{"key":"ref_43","unstructured":"Yu, F., and Koltun, V. (2015). Multi-scale context aggregation by dilated convolutions. arXiv."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Yu, F., Koltun, V., and Funkhouser, T. (2017, January 21\u201326). Dilated residual networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.75"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1007\/978-3-030-01252-6_24","article-title":"Receptive Field Block Net for Accurate and Fast Object Detection","volume":"11215","author":"Liu","year":"2018","journal-title":"Lect. Notes Comput. Sci."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Mehta, S., Rastegari, M., Shapiro, L., and Hajishirzi, H. (2019, January 16\u201320). ESPNetv2: A light-weight, power efficient, and general purpose convolutional neural network. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00941"},{"key":"ref_47","unstructured":"Takikawa, T., Acuna, D., Jampani, V., and Fidler, S. (November, January 27). Gated-scnn: Gated shape cnns for semantic segmentation. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Korea."},{"key":"ref_48","unstructured":"Li, Y., Chen, Y., Wang, N., and Zhang, Z.X. (November, January 27). Scale-aware trident networks for object detection. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Korea."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"5701","DOI":"10.1109\/TGRS.2019.2901737","article-title":"A 3-D Atrous Convolution Neural Network for Hyperspectral Image Denoising","volume":"57","author":"Liu","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Chen, H., Lin, M., Zhang, H., Yang, G., Xia, G.S., Zheng, X., and Zhang, L. (August, January 28). Multi-level fusion of the multi-receptive fields contextual networks and disparity network for pairwise semantic stereo. Proceedings of the 2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8899306"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"3532","DOI":"10.1109\/TGRS.2020.3009143","article-title":"Adaptive Effective Receptive Field Convolution for Semantic Segmentation of VHR Remote Sensing Images","volume":"59","author":"Chen","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_52","first-page":"5999","article-title":"Attention is all you need","volume":"2017","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201322). Squeeze-and-Excitation Networks. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"540","DOI":"10.1109\/TMI.2018.2867261","article-title":"Recalibrating fully convolutional networks with spatial and channel \u201csqueeze and excitation\u201d blocks","volume":"38","author":"Roy","year":"2018","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/978-3-030-01234-2_1","article-title":"CBAM: Convolutional block attention module","volume":"11211","author":"Woo","year":"2018","journal-title":"Lect. Notes Comput. Sci."},{"key":"ref_56","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 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00326"},{"key":"ref_57","unstructured":"Leibe, B., Matas, J., Sebe, N., and Welling, M. (2016). Identity Mappings in Deep Residual Networks. European Conference on Computer Vision, Springer International Publishing."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Zhang, M., Hu, X., Zhao, L., Lv, Y., Luo, M., and Pang, S. (2017). Learning dual multi-scale manifold ranking for semantic segmentation of high-resolution images. Remote Sens., 9.","DOI":"10.20944\/preprints201704.0061.v1"},{"key":"ref_59","unstructured":"Kingma, D.P., and Ba, J.L. (2015, January 7\u20139). Adam: A method for stochastic optimization. Proceedings of the 3rd International Conference on Learning Representations, ICLR 2015\u2014Conference Track Proceedings, San Diego, CA, USA."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"949","DOI":"10.1016\/0895-4356(88)90031-5","article-title":"A rEAPPRAISAL of the kappa coefficient","volume":"41","author":"Thompson","year":"1988","journal-title":"J. Clin. Epidemiol."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Berman, M., Rannen Triki, A., and Blaschko, M.B. (2018, January 18\u201322). The lov\u00e1sz-softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00464"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/23\/4902\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:39:14Z","timestamp":1760168354000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/23\/4902"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,3]]},"references-count":61,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["rs13234902"],"URL":"https:\/\/doi.org\/10.3390\/rs13234902","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12,3]]}}}