{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T18:29:15Z","timestamp":1771007355622,"version":"3.50.1"},"reference-count":89,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2019,9,14]],"date-time":"2019-09-14T00:00:00Z","timestamp":1568419200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Chinese Academy of Sciences","award":["XDA19040501"],"award-info":[{"award-number":["XDA19040501"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41471376"],"award-info":[{"award-number":["41471376"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Semantic segmentation is a fundamental means of extracting information from remotely sensed images at the pixel level. Deep learning has enabled considerable improvements in efficiency and accuracy of semantic segmentation of general images. Typical models range from benchmarks such as fully convolutional networks, U-Net, Micro-Net, and dilated residual networks to the more recently developed DeepLab 3+. However, many of these models were originally developed for segmentation of general or medical images and videos, and are not directly relevant to remotely sensed images. The studies of deep learning for semantic segmentation of remotely sensed images are limited. This paper presents a novel flexible autoencoder-based architecture of deep learning that makes extensive use of residual learning and multiscaling for robust semantic segmentation of remotely sensed land-use images. In this architecture, a deep residual autoencoder is generalized to a fully convolutional network in which residual connections are implemented within and between all encoding and decoding layers. Compared with the concatenated shortcuts in U-Net, these residual connections reduce the number of trainable parameters and improve the learning efficiency by enabling extensive backpropagation of errors. In addition, resizing or atrous spatial pyramid pooling (ASPP) can be leveraged to capture multiscale information from the input images to enhance the robustness to scale variations. The residual learning and multiscaling strategies improve the trained model\u2019s generalizability, as demonstrated in the semantic segmentation of land-use types in two real-world datasets of remotely sensed images. Compared with U-Net, the proposed method improves the Jaccard index (JI) or the mean intersection over union (MIoU) by 4-11% in the training phase and by 3-9% in the validation and testing phases. With its flexible deep learning architecture, the proposed approach can be easily applied for and transferred to semantic segmentation of land-use variables and other surface variables of remotely sensed images.<\/jats:p>","DOI":"10.3390\/rs11182142","type":"journal-article","created":{"date-parts":[[2019,9,16]],"date-time":"2019-09-16T03:17:57Z","timestamp":1568603877000},"page":"2142","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Deep Residual Autoencoder with Multiscaling for Semantic Segmentation of Land-Use Images"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9382-8637","authenticated-orcid":false,"given":"Lianfa","family":"Li","sequence":"first","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information Systems, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Datun Road, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,9,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1364\/ON.15.5.000008","article-title":"Integrating visual cues for object segmentation and recognition","volume":"15","author":"Edelman","year":"1989","journal-title":"Opt. News"},{"key":"ref_2","unstructured":"Ohta, Y.-I., Kanade, T., and Sakai, T. (1978, January 7\u201310). An analysis system for scenes containing objects with substructures. Proceedings of the Fourth International Joint Conference on Pattern Recognitions, Kyoto, Japan."},{"key":"ref_3","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."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1007\/978-981-10-7521-6_30","article-title":"Deep Learning and Machine Learning for Object Detection in Remote Sensing Images","volume":"473","author":"Yang","year":"2018","journal-title":"Lect. Notes Electr. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MGRS.2017.2762307","article-title":"Deep Learning in Remote Sensing","volume":"5","author":"Zhu","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Bishop, M.C. (1995). Neural Networks for Pattern Recognition, Oxford University Press.","DOI":"10.1093\/oso\/9780198538493.001.0001"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1561\/2000000039","article-title":"Deep learning: Methods and applications","volume":"7","author":"Deng","year":"2014","journal-title":"Found. Trends\u00ae Signal Process."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1038\/nature14236","article-title":"Human-level control through deep reinforcement learning","volume":"518","author":"Mnih","year":"2015","journal-title":"Nature"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1038\/nature14179","article-title":"A direct GABAergic output from the basal ganglia to frontal cortex","volume":"521","author":"Saunders","year":"2015","journal-title":"Nature"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Garcia-Garcia, A., Orts-Escolano, S., Oprea, S., Villena-Martinez, V., and Garcia-Rodriguez, J. (2017). A review on deep learning techniques applied to semantic segmentation. arXiv.","DOI":"10.1016\/j.asoc.2018.05.018"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.neucom.2018.03.037","article-title":"Methods and datasets on semantic segmentation: A review","volume":"304","author":"Yu","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"9375","DOI":"10.1109\/ACCESS.2017.2788044","article-title":"Deep Learning Applications in Medical Image Analysis","volume":"6","author":"Ker","year":"2018","journal-title":"IEEE Access"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","article-title":"A survey on deep learning in medical image analysis","volume":"42","author":"Litjens","year":"2017","journal-title":"Med. Image Anal."},{"key":"ref_15","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT Press."},{"key":"ref_16","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_17","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_18","unstructured":"Yu, F., and Koltun, V. (2015). Multi-scale context aggregation by dilated convolutions. arXiv."},{"key":"ref_19","unstructured":"Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., and Yuille, A.L. (2014). Semantic image segmentation with deep convolutional nets and fully connected crfs. arXiv."},{"key":"ref_20","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":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_21","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 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.549"},{"key":"ref_22","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_23","doi-asserted-by":"crossref","unstructured":"Peng, C., Zhang, X., Yu, G., Luo, G., and Sun, J. (2017, January 21\u201326). Large Kernel Matters--Improve Semantic Segmentation by Global Convolutional Network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.189"},{"key":"ref_24","unstructured":"Chen, L.-C., Papandreou, G., Schroff, F., and Adam, H. (2017). Rethinking atrous convolution for semantic image segmentation. arXiv."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Panboonyuen, T., Jitkajornwanich, K., Lawawirojwong, S., Srestasathiern, P., and Vateekul, P. (2019). Semantic Segmentation on Remotely Sensed Images Using an Enhanced Global Convolutional Network with Channel Attention and Domain Specific Transfer Learning. Remote Sens., 11.","DOI":"10.20944\/preprints201812.0090.v3"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Kang, M., Lin, Z., Leng, X.G., and Ji, K.F. (2017, January 18\u201321). A Modified Faster R-CNN Based on CFAR Algorithm for SAR Ship Detection. Proceedings of the 2017 International Workshop on Remote Sensing with Intelligent Processing (Rsip 2017), Shanghai, China.","DOI":"10.1109\/RSIP.2017.7958815"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"5217","DOI":"10.1109\/TGRS.2018.2812619","article-title":"Two-Stage Convolutional Neural Network for Ship and Spill Detection Using SLAR Images","volume":"56","author":"Gallego","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"4973","DOI":"10.1109\/TGRS.2018.2803038","article-title":"Oil Spill Segmentation via Adversarial f-Divergence Learning","volume":"56","author":"Yu","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"6054","DOI":"10.1109\/TGRS.2017.2719738","article-title":"Learning Aerial Image Segmentation From Online Maps","volume":"55","author":"Kaiser","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1109\/LGRS.2018.2802944","article-title":"Road Extraction by Deep Residual U-Net","volume":"15","author":"Zhang","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1109\/MGRS.2016.2540798","article-title":"Deep Learning for Remote Sensing Data A technical tutorial on the state of the art","volume":"4","author":"Zhang","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"234","DOI":"10.2307\/143141","article-title":"A computer movie simulating urban growth in the Detroit region","volume":"46","author":"Tobler","year":"1970","journal-title":"Econ. Geogr."},{"key":"ref_33","unstructured":"Kr\u00e4henb\u00fchl, P., and Koltun, V. (2011). Efficient inference in fully connected crfs with gaussian edge potentials. arXiv."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Pan, X., and Zhao, J. (2018). High-resolution remote sensing image classification method based on convolutional neural network and restricted conditional random field. Remote Sens., 10.","DOI":"10.3390\/rs10060920"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"754","DOI":"10.1109\/LGRS.2018.2804345","article-title":"High-Resolution Remote Sensing Image Classification Using Associative Hierarchical CRF Considering Segmentation Quality","volume":"15","author":"Yang","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"He, K.M., Zhang, X.Y., Ren, S.Q., 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_37","doi-asserted-by":"crossref","first-page":"630","DOI":"10.1007\/978-3-319-46493-0_38","article-title":"Identity Mappings in Deep Residual Networks","volume":"9908","author":"He","year":"2016","journal-title":"Lect. Notes Comput. Sci."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Li, L.F. (2019). Geographically Weighted Machine Learning and Downscaling for High-Resolution Spatiotemporal Estimations of Wind Speed. Remote Sens., 11.","DOI":"10.3390\/rs11111378"},{"key":"ref_39","unstructured":"Li, L., Fang, Y., Wu, J., Wang, C., and Ge, Y. (2019). Autoencoder based deep residual networks for robust regression and spatiotemporal estimation. IEEE Trans. Nerual Netw. Learn. Syst., under review."},{"key":"ref_40","unstructured":"(2018, December 01). Dstl Satellite Imagery Feature Detection. Available online: https:\/\/www.kaggle.com\/c\/dstl-satellite-imagery-feature-detection\/overview\/description."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Volpi, M., and Ferrari, V. (2015, January 12). Semantic segmentation of urban scenes by learning local class interactions. Proceedings of the IEEE CVPR 2015 Workshop \u201cLooking from above: When Earth observation meets vision\u201d (EARTHVISION), Boston, MA, USA.","DOI":"10.1109\/CVPRW.2015.7301377"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"9938","DOI":"10.1073\/pnas.1301691110","article-title":"Prefrontal microcircuit underlies contextual learning after hippocampal loss","volume":"110","author":"Zelikowsky","year":"2013","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_43","unstructured":"Srivastava, K.R., Greff, K., and Schmidhuber, J. (2015). Highway networks. arXiv."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1704","DOI":"10.1109\/TPAMI.2011.235","article-title":"Aggregating local image descriptors into compact codes","volume":"34","author":"Jegou","year":"2011","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_45","unstructured":"Szeliski, R. (August, January 30). Locally adapted hierarchical basis preconditioning. Proceedings of the SIGGRAPH\u201906, Boston, MA, USA."},{"key":"ref_46","unstructured":"Veit, A., Wilber, M., and Belongie, S. (2016, January 5\u201310). Residual networks behave like ensembles of relatively shallow networks. Proceedings of the 30th International Conference on Neural Information Processing Systems, Barcelona, Spain."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Li, K., Li, K., Wang, L., Zhong, B., and Fu, Y. (2018, January 8\u201314). Image super-resolution using very deep residual channel attention networks. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_18"},{"key":"ref_48","unstructured":"Alexandre, D., Chang, C.-P., Peng, W.-H., and Hang, H.-M. (2018, January 18\u201322). An Autoencoder-based Learned Image Compressor: Description of Challenge Proposal by NCTU. Proceedings of the CVPR Workshops, Salt Lake City, UT, USA."},{"key":"ref_49","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_50","doi-asserted-by":"crossref","unstructured":"Taylor, G.W., Fergus, R., LeCun, Y., and Bregler, C. (2010). Convolutional learning of spatio-temporal features. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-642-15567-3_11"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Lea, C., Vidal, R., Reiter, A., and Hager, G.D. (2016). Temporal convolutional networks: A unified approach to action segmentation. European Conference on Computer Vision, Springer.","DOI":"10.1109\/CVPR.2017.113"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Zhang, J., Zheng, Y., and Qi, D. (2017, January 4\u20139). Deep spatio-temporal residual networks for citywide crowd flows prediction. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, San Francisco, CA, USA.","DOI":"10.1609\/aaai.v31i1.10735"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Zhang, R., Li, N., Huang, S., Xie, P., and Jiang, H. (2017). Automatic Prediction of Traffic Flow Based on Deep Residual Networks. International Conference on Mobile Ad-Hoc and Sensor Networks, Springer.","DOI":"10.1007\/978-981-10-8890-2_24"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Xi, G., Yin, L., Li, Y., and Mei, S. (2018, January 6\u20139). A Deep Residual Network Integrating Spatial-temporal Properties to Predict Influenza Trends at an Intra-urban Scale. Proceedings of the 2nd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, Seattle, WA, USA.","DOI":"10.1145\/3281548.3281558"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Tran, L., Liu, X., Zhou, J., and Jin, R. (2017, January 21\u201326). Missing modalities imputation via cascaded residual autoencoder. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.528"},{"key":"ref_56","unstructured":"Raj, A., Maturana, D., and Scherer, S. (2015). Multi-Scale Convolutional Architecture for Semantic Segmentation, Robotics Institute, Carnegie Mellon University. Tech Rep CMU-RITR-15-21."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Roy, A., and Todorovic, S. (2016). A multi-scale cnn for affordance segmentation in rgb images. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-319-46493-0_12"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Eigen, D., and Fergus, R. (2015, January 7\u201313). Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture. Proceedings of the IEEE International Conference on Computer Vision 2015, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.304"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Bian, X., Lim, S.N., and Zhou, N. (2016, January 7\u201310). Multiscale fully convolutional network with application to industrial inspection. Proceedings of the 2016 IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Placid, NY, USA.","DOI":"10.1109\/WACV.2016.7477595"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1016\/j.media.2018.12.003","article-title":"Micro-Net: A unified model for segmentation of various objects in microscopy images","volume":"52","author":"Raza","year":"2019","journal-title":"Med. Image Anal."},{"key":"ref_61","unstructured":"Zhou, S., Wu, J.-N., Wu, Y., and Zhou, X. (2015). Exploiting local structures with the kronecker layer in convolutional networks. arXiv."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Chen, L.-C., 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_63","unstructured":"Fink, M., and Perona, P. (2004). Mutual boosting for contextual inference. Advances in Neural Information Processing Systems, MIT Press."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Shotton, J., Johnson, M., and Cipolla, R. (2008, January 23\u201328). Semantic texton forests for image categorization and segmentation. Proceedings of the 2008 IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, AK, USA.","DOI":"10.1109\/CVPR.2008.4587503"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Fulkerson, B., Vedaldi, A., and Soatto, S. (October, January 29). Class segmentation and object localization with superpixel neighborhoods. Proceedings of the 2009 IEEE 12th International Conference on Computer Vision, Kyoto, Japan.","DOI":"10.1109\/ICCV.2009.5459175"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Silberman, N., and Fergus, R. (2011, January 6\u201313). Indoor scene segmentation using a structured light sensor. Proceedings of the 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), Barcelona, Spain.","DOI":"10.1109\/ICCVW.2011.6130298"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1007\/s11263-008-0202-0","article-title":"Robust higher order potentials for enforcing label consistency","volume":"82","author":"Kohli","year":"2009","journal-title":"Int. J. Comput. Vis."},{"key":"ref_68","unstructured":"Torralba, A., Murphy, K.P., and Freeman, W.T. (2005). Contextual models for object detection using boosted random fields. Advances in Neural Information Processing Systems, MIT Press."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"1915","DOI":"10.1109\/TPAMI.2012.231","article-title":"Learning hierarchical features for scene labeling","volume":"35","author":"Farabet","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Alvarez, J.M., LeCun, Y., Gevers, T., and Lopez, A.M. (2012). Semantic road segmentation via multi-scale ensembles of learned features. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-642-33868-7_58"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Noh, H., Hong, S., and Han, B. (2015, January 7\u201313). Learning deconvolution network for semantic segmentation. Proceedings of the IEEE International Conference on Computer Vision 2015, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.178"},{"key":"ref_72","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_73","doi-asserted-by":"crossref","unstructured":"Zheng, S., Jayasumana, S., Romera-Paredes, B., Vineet, V., Su, Z., Du, D., Huang, C., and Torr, P.H. (2015, January 7\u201313). Conditional random fields as recurrent neural networks. Proceedings of the IEEE International Conference on Computer Vision 2015, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.179"},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Arnab, A., Jayasumana, S., Zheng, S., and Torr, P.H. (2016). Higher order conditional random fields in deep neural networks. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-319-46475-6_33"},{"key":"ref_75","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20138). ImageNet classification with deep convolutional neural networks. Proceedings of the NIPS 2012, Lake Tahoe, NV, USA."},{"key":"ref_76","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Szegedy, C. (2015, January 8\u201310). Going deeper with convolutions. Proceedings of the CVPR 2015, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"3150","DOI":"10.1016\/j.neucom.2008.04.030","article-title":"Modeling word perception using the Elman network","volume":"71","author":"Liou","year":"2008","journal-title":"Neurocomputing"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.neucom.2013.09.055","article-title":"Autoencoder for words","volume":"139","author":"Liou","year":"2014","journal-title":"Neurocomputing"},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Jolliffe, I. (2011). Principal Component Analysis, Springer.","DOI":"10.1007\/978-3-642-04898-2_455"},{"key":"ref_81","unstructured":"Fang, Y., and Li, L. (2019). Estimation of high-precision high-resolution meteorological factors based on machine learning. J. Geo-Inf. Sci., (In Chinese)."},{"key":"ref_82","first-page":"1","article-title":"Automatic differentiation in machine learning: A survey","volume":"18","author":"Baydin","year":"2018","journal-title":"J. Mach. Learn. Res."},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Papandreou, G., Kokkinos, I., and Savalle, P.-A. (2015, January 7\u201312). Modeling local and global deformations in deep learning: Epitomic convolution, multiple instance learning, and sliding window detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298636"},{"key":"ref_84","unstructured":"Iglovikov, V., Mushinskiy, S., and Osin, V. (2017). Satellite imagery feature detection using deep convolutional neural network: A kaggle competition. arXiv."},{"key":"ref_85","unstructured":"Bishop, M.C. (2006). Pattern Recognition and Machine Learning, Springer."},{"key":"ref_86","unstructured":"Padwick, C., Deskevich, M., Pacifici, F., and Smallwood, S. (2010, January 26\u201330). WorldView-2 pan-sharpening. Proceedings of the ASPRS 2010 Annual Conference, San Diego, CA, USA."},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Rhu, M., Gimelshein, N., Clemons, J., Zulfiqar, A., and Keckler, S.W. (2016, January 15\u201319). vDNN: Virtualized deep neural networks for scalable, memory-efficient neural network design. Proceedings of the 49th Annual IEEE\/ACM International Symposium on Microarchitecture, Taipei, Taiwan.","DOI":"10.1109\/MICRO.2016.7783721"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"102897","DOI":"10.1016\/j.earscirev.2019.102897","article-title":"Principles and methods of scaling geospatial Earth science data","volume":"197","author":"Ge","year":"2019","journal-title":"Earth-Sci. Rev."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"659","DOI":"10.1109\/TGRS.2014.2326886","article-title":"Conditional random fields for multitemporal and multiscale classification of optical satellite imagery","volume":"53","author":"Hoberg","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/18\/2142\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:20:04Z","timestamp":1760188804000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/18\/2142"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,9,14]]},"references-count":89,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2019,9]]}},"alternative-id":["rs11182142"],"URL":"https:\/\/doi.org\/10.3390\/rs11182142","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,9,14]]}}}