{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,22]],"date-time":"2026-03-22T07:29:55Z","timestamp":1774164595085,"version":"3.50.1"},"reference-count":37,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2018,11,27]],"date-time":"2018-11-27T00:00:00Z","timestamp":1543276800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Natural Science Foundations of China","award":["41601480"],"award-info":[{"award-number":["41601480"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The interpretation of land use and land cover (LULC) is an important issue in the fields of high-resolution remote sensing (RS) image processing and land resource management. Fully training a new or existing convolutional neural network (CNN) architecture for LULC classification requires a large amount of remote sensing images. Thus, fine-tuning a pre-trained CNN for LULC detection is required. To improve the classification accuracy for high resolution remote sensing images, it is necessary to use another feature descriptor and to adopt a classifier for post-processing. A fully connected conditional random fields (FC-CRF), to use the fine-tuned CNN layers, spectral features, and fully connected pairwise potentials, is proposed for image classification of high-resolution remote sensing images. First, an existing CNN model is adopted, and the parameters of CNN are fine-tuned by training datasets. Then, the probabilities of image pixels belong to each class type are calculated. Second, we consider the spectral features and digital surface model (DSM) and combined with a support vector machine (SVM) classifier, the probabilities belong to each LULC class type are determined. Combined with the probabilities achieved by the fine-tuned CNN, new feature descriptors are built. Finally, FC-CRF are introduced to produce the classification results, whereas the unary potentials are achieved by the new feature descriptors and SVM classifier, and the pairwise potentials are achieved by the three-band RS imagery and DSM. Experimental results show that the proposed classification scheme achieves good performance when the total accuracy is about 85%.<\/jats:p>","DOI":"10.3390\/rs10121889","type":"journal-article","created":{"date-parts":[[2018,11,27]],"date-time":"2018-11-27T12:17:35Z","timestamp":1543321055000},"page":"1889","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Fully Connected Conditional Random Fields for High-Resolution Remote Sensing Land Use\/Land Cover Classification with Convolutional Neural Networks"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2190-6253","authenticated-orcid":false,"given":"Bin","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Geography and Information Engineering, China University of Geoscience, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cunpeng","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Geography and Information Engineering, China University of Geoscience, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yonglin","family":"Shen","sequence":"additional","affiliation":[{"name":"School of Geography and Information Engineering, China University of Geoscience, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yueyan","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Geography and Information Engineering, China University of Geoscience, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,11,27]]},"reference":[{"key":"ref_1","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":"2016","journal-title":"Pattern Recognit."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MGRS.2017.2762307","article-title":"Deep Learning in Remote Sensing: A Review [PDF]","volume":"5","author":"Zhu","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2094","DOI":"10.1109\/JSTARS.2014.2329330","article-title":"Deep learning-based classification of hyperspectral data","volume":"7","author":"Chen","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Nogueira, K., Miranda, W.O., and Santos, J.A.D. (2015, January 26\u201329). Improving Spatial Feature Representation from Aerial Scenes by Using Convolutional Network. Proceedings of the 28th SIBGRAPI Conference on Graphics, Patterns and Images, Salvador, Bahia, Brazil.","DOI":"10.1109\/SIBGRAPI.2015.39"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"468","DOI":"10.1080\/2150704X.2015.1047045","article-title":"Spectral\u2013spatial classification of hyperspectral images using deep convolutional neural networks","volume":"6","author":"Yue","year":"2015","journal-title":"Remote Sens. Lett."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Maggiori, E., Tarabalka, Y., Charpiat, G., and Alliez, P. (2016). Convolutional Neural Networks for Large-Scale Remote-Sensing Image Classification. IEEE Trans. Geosci. Remote Sens., 55.","DOI":"10.1109\/IGARSS.2016.7730322"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Makantasis, K., Karantzalos, K., Doulamis, A., and Doulamis, N. (2015, January 26\u201331). Deep Supervised Learning for Hyperspectral data Classification Through Convolutional Neural Networks. Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy.","DOI":"10.1109\/IGARSS.2015.7326945"},{"key":"ref_8","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":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","first-page":"627","article-title":"Land Use Classification in Remote Sensing Images by Convolutional Neural Networks","volume":"28","author":"Castelluccio","year":"2015","journal-title":"Acta Ecol. Sinica"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23\u201328). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_11","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2012","journal-title":"Commun. ACM"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Chatfield, K., Simonyan, K., Vedaldi, A., and Zisserman, A. (2014, January 1\u20135). Return of the Devil in the Details: Delving Deep into Convolutional Nets. Proceedings of the British Machine Vision Conference 2014, Nottingham, UK.","DOI":"10.5244\/C.28.6"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Xie, M., Jean, N., Burke, M., and Ermon, S. (2016, January 12\u201317). Transfer learning from deep features for remote sensing and poverty mapping. Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, Phoenix, AZ, USA.","DOI":"10.1609\/aaai.v30i1.9906"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Penatti, O.A.B., Nogueira, K., and Santos, J.A.D. (2015, January 7\u201312). Do deep features generalize from everyday objects to remote sensing and aerial scenes domains?. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Boston, MA, USA.","DOI":"10.1109\/CVPRW.2015.7301382"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"14680","DOI":"10.3390\/rs71114680","article-title":"Transferring Deep Convolutional Neural Networks for the Scene Classification of High-Resolution Remote Sensing Imagery","volume":"7","author":"Hu","year":"2015","journal-title":"Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"11372","DOI":"10.3390\/rs61111372","article-title":"Object-Based Land-Cover Mapping with High Resolution Aerial Photography at a County Scale in Midwestern USA","volume":"6","author":"Li","year":"2014","journal-title":"Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"905","DOI":"10.1080\/01431160701311309","article-title":"Object-oriented classification of side-scan sonar data for mapping benthic marine habitats","volume":"29","author":"Lucieer","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.isprsjprs.2009.06.004","article-title":"Object based image analysis for remote sensing","volume":"65","author":"Blaschke","year":"2010","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1109\/JSTARS.2012.2232904","article-title":"Hyperspectral imagery restoration using nonlocal spectral-spatial structured sparse representation with noise estimation","volume":"6","author":"Qian","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2381","DOI":"10.1109\/JSTARS.2015.2388577","article-title":"Spectral-Spatial classification of hyperspectral data based on deep belief network","volume":"8","author":"Chen","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_21","first-page":"309","article-title":"GrabCut: Interactive foreground extraction using iterated graph cuts","volume":"Volume 23","author":"Rother","year":"2004","journal-title":"Proceedings of the 31st International conference on computer graphic and interactive techniques SIGGRAPH \u201904 ACM SIGGRAPH"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1007\/s11263-007-0109-1","article-title":"Textonboost for image understanding: Multi-class object recognition and segmentation by jointly modeling texture, layout, and context","volume":"81","author":"Shotton","year":"2009","journal-title":"Int. J. Comput. Vis."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Lucchi, A., Li, Y., Boix, X., Smith, K., and Fua, P. (2011, January 6\u201313). Are spatial and global constraints really necessary for segmentation?. Proceedings of the IEEE International Conference on Computer Vision, Barcelona, Spain.","DOI":"10.1109\/ICCV.2011.6126219"},{"key":"ref_24","unstructured":"He, X., Zemel, R.S., and Carreira-Perpindn, M. (July, January 27). Multiscale conditional random fields for image labeling. Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004, Washington, DC, USA."},{"key":"ref_25","unstructured":"Ladicky, L., Russell, C., Kohli, P., and Torr, P.H. (October, January 29). Associative hierarchical crfs for object class image segmentation. Proceedings of the IEEE 12th International Conference on Computer Vision, Kyoto, Japan."},{"key":"ref_26","unstructured":"Lempitsky, V., Vedaldi, A., and Zisserman, A. (2011, January 12\u201317). Pylon Model for Semantic Segmentation. Proceedings of the Neural Information Processing Systems NIPS 2011, Granada, Spain."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Delong, A., Osokin, A., Isack, H.N., and Boykov, Y. (2010, January 13\u201318). Fast approximate energy minimization with label costs. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA.","DOI":"10.1109\/CVPR.2010.5539897"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Gonfaus, J.M., Boix, X., Van de Weijer, J., Bagdanov, A.D., Serrat, J., and Gonz\u00e0lez, J. (2010, January 13\u201318). Harmony potentials for joint classification and segmentation. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA.","DOI":"10.1109\/CVPR.2010.5540048"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1777","DOI":"10.3390\/rs3081777","article-title":"Segment-Based Land Cover Mapping of a Suburban Area\u2014Comparison of High-Resolution Remotely Sensed Datasets Using Classification Trees and Test Field Points","volume":"3","author":"Matikainen","year":"2011","journal-title":"Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1793","DOI":"10.1016\/j.asr.2008.02.012","article-title":"Classification of hyperspectral remote-sensing data with primal SVM for small-sized training dataset problem","volume":"41","author":"Chi","year":"2008","journal-title":"Adv. Space Res."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1473","DOI":"10.14358\/PERS.74.12.1473","article-title":"A Knowledge-based Approach to Urban Feature Classification Using Aerial Imagery with Lidar Data","volume":"74","author":"Huang","year":"2008","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1007\/s11517-006-0141-2","article-title":"Segmentation of retinal blood vessels using a novel clustering algorithm (RACAL) with a partial supervision strategy","volume":"45","author":"Salem","year":"2007","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1109\/TBME.2016.2535311","article-title":"A Discriminatively Trained Fully Connected Conditional Random Field Model for Blood Vessel Segmentation in Fundus Images","volume":"64","author":"Orlando","year":"2016","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2861","DOI":"10.1109\/TAES.2016.160061","article-title":"SAR ATR by a combination of convolutional neural network and support vector machines","volume":"52","author":"Wagner","year":"2018","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_35","unstructured":"Gerke, M. (2015). Use of the Stair Vision Library Within the ISPRS 2D Semantic Labeling Benchmark (Vaihingen), Researche Gate. Technical Report, University of Twente."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2092","DOI":"10.1109\/LGRS.2017.2752750","article-title":"Marta gans: Unsupervised representation learning for remote sensing image classification","volume":"14","author":"Lin","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_37","unstructured":"Radford, A., Metz, L., and Chintala, S. (2016, January 2\u20134). Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. Proceedings of the International Conference on Learning Representations (ICLR), San Juan, Puerto Rico."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/12\/1889\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:32:33Z","timestamp":1760196753000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/12\/1889"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,11,27]]},"references-count":37,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2018,12]]}},"alternative-id":["rs10121889"],"URL":"https:\/\/doi.org\/10.3390\/rs10121889","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,11,27]]}}}