{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T16:51:17Z","timestamp":1772556677372,"version":"3.50.1"},"reference-count":74,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2016,4,14]],"date-time":"2016-04-14T00:00:00Z","timestamp":1460592000000},"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>The availability of high-resolution remote sensing (HRRS) data has opened up the possibility for new interesting applications, such as per-pixel classification of individual objects in greater detail. This paper shows how a convolutional neural network (CNN) can be applied to multispectral orthoimagery and a digital surface model (DSM) of a small city for a full, fast and accurate per-pixel classification. The predicted low-level pixel classes are then used to improve the high-level segmentation. Various design choices of the CNN architecture are evaluated and analyzed. The investigated land area is fully manually labeled into five categories (vegetation, ground, roads, buildings and water), and the classification accuracy is compared to other per-pixel classification works on other land areas that have a similar choice of categories. The results of the full classification and segmentation on selected segments of the map show that CNNs are a viable tool for solving both the segmentation and object recognition task for remote sensing data.<\/jats:p>","DOI":"10.3390\/rs8040329","type":"journal-article","created":{"date-parts":[[2016,4,14]],"date-time":"2016-04-14T12:37:04Z","timestamp":1460637424000},"page":"329","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":253,"title":["Classification and Segmentation of Satellite Orthoimagery Using Convolutional Neural Networks"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0579-7181","authenticated-orcid":false,"given":"Martin","family":"L\u00e4ngkvist","sequence":"first","affiliation":[{"name":"Applied Autonomous Sensor Systems, \u00d6rebro University, Fakultetsgatan 1, \u00d6rebro 701 82, Sweden"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0305-3728","authenticated-orcid":false,"given":"Andrey","family":"Kiselev","sequence":"additional","affiliation":[{"name":"Applied Autonomous Sensor Systems, \u00d6rebro University, Fakultetsgatan 1, \u00d6rebro 701 82, Sweden"}]},{"given":"Marjan","family":"Alirezaie","sequence":"additional","affiliation":[{"name":"Applied Autonomous Sensor Systems, \u00d6rebro University, Fakultetsgatan 1, \u00d6rebro 701 82, Sweden"}]},{"given":"Amy","family":"Loutfi","sequence":"additional","affiliation":[{"name":"Applied Autonomous Sensor Systems, \u00d6rebro University, Fakultetsgatan 1, \u00d6rebro 701 82, Sweden"}]}],"member":"1968","published-online":{"date-parts":[[2016,4,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"703","DOI":"10.2307\/3235884","article-title":"Measuring phenological variability from satellite imagery","volume":"5","author":"Reed","year":"1994","journal-title":"J. Veg. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1641\/0006-3568(2004)054[0547:ACSMOG]2.0.CO;2","article-title":"A continuous satellite-derived measure of global terrestrial primary production","volume":"54","author":"Running","year":"2004","journal-title":"BioScience"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1016\/j.rse.2005.01.003","article-title":"Hyperspectral data processing for repeat detection of small infestations of leafy spurge","volume":"95","author":"Glenn","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1586","DOI":"10.1109\/TGRS.2004.826822","article-title":"Georegistration of Landsat data via robust matching of multiresolution features","volume":"42","author":"Netanyahu","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","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":"IEEE Proc."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"467","DOI":"10.1016\/j.rse.2003.11.005","article-title":"Estimation of land surface temperature-vegetation abundance relationship for urban heat island studies","volume":"89","author":"Weng","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1301","DOI":"10.1016\/j.rse.2011.01.009","article-title":"Object-based crop identification using multiple vegetation indices, textural features and crop phenology","volume":"115","author":"Ngugi","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1037","DOI":"10.1007\/s12665-009-0245-8","article-title":"Delineation of landslide hazard areas on Penang Island, Malaysia, by using frequency ratio, logistic regression, and artificial neural network models","volume":"60","author":"Pradhan","year":"2010","journal-title":"Environ. Earth Sci."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Yang, Y., and Newsam, S. (2010, January 2\u20135). Bag-of-visual-words and spatial extensions for land-use classification. Proceedings of the ACM International Symposium on Advances in Geographic Information Systems, San Jose, CA, USA.","DOI":"10.1145\/1869790.1869829"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Lai, K., Bo, L., Ren, X., and Fox, D. (2011, January 9\u201313). A large-scale hierarchical multi-view RGB-D object dataset. Proceedings of the IEEE International Conference on Robotics and Automation, Shanghai, China.","DOI":"10.1109\/ICRA.2011.5980382"},{"key":"ref_11","unstructured":"Xia, G.S., Yang, W., Delon, J., Gousseau, Y., Sun, H., and Ma\u00eetre, H. (2010, January 5\u20137). Structural high-resolution satellite image indexing. Proceedings of the ISPRS TC VII Symposium-100 Years ISPRS, Vienna, Austria."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2770","DOI":"10.1109\/TGRS.2012.2219314","article-title":"Latent Dirichlet Allocation for spatial analysis of satellite images","volume":"51","author":"Vaduva","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"818","DOI":"10.1109\/TGRS.2012.2205158","article-title":"Geographic image retrieval using local invariant features","volume":"51","author":"Yang","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Yang, Y., and Newsam, S. (2008, January 7\u201311). Comparing SIFT descriptors and gabor texture features for classification of remote sensed imagery. Proceedings of the International Conference on Image Processing (ICIP), Cairo, Egypt.","DOI":"10.1109\/ICIP.2008.4712139"},{"key":"ref_15","unstructured":"Dos Santos, J., Penatti, O., and Da Torres, R. (2010, January 17\u201321). Evaluating the potential of texture and color descriptors for remote sensing image retrieval and classification. Proceedings of the International Conference on Computer Vision Theory and Applications, Angers, France."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"LeCun","year":"1998","journal-title":"IEEE Proc."},{"key":"ref_17","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012). Advances in Neural Information Processing Systems, Curran Associates."},{"key":"ref_18","unstructured":"Socher, R., Huval, B., Bath, B., Manning, C.D., and Ng, A.Y. (2012). Advances in Neural Information Processing Systems, Curran Associates."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Quigley, M., Batra, S., Gould, S., Klingbeil, E., Le, Q.V., Wellman, A., and Ng, A.Y. (2009, January 12\u201317). High-accuracy 3D sensing for mobile manipulation: improving object detection and door opening. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Kobe, Japan.","DOI":"10.1109\/ROBOT.2009.5152750"},{"key":"ref_20","unstructured":"Couprie, C., Farabet, C., LeCun, Y., and Najman, L. (2013, January 2\u20134). Indoor Semantic Segmentation using depth information. Proceedings of the International Conference on Learning Representation, Scottsdale, Arizona."},{"key":"ref_21","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":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_22","unstructured":"Castelluccio, M., Poggi, G., Sansone, C., and Verdoliva, L. Land Use Classification in Remote Sensing Images by Convolutional Neural Networks, Available online: http:\/\/arxiv.org\/abs\/1508.00092."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Penatti, O.A., Nogueira, K., and dos Santos, J.A. (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_24","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_25","doi-asserted-by":"crossref","unstructured":"Nguyen, T., Han, J., and Park, D.C. (2013, January 7\u201310). Satellite image classification using convolutional learning. Proceedings of the AIP Conference, Albuquerque, NM, USA.","DOI":"10.1063\/1.4825984"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"17","DOI":"10.3176\/eng.2011.1.03","article-title":"Ortophoto analysis for UGV long-range autonomous navigation","volume":"17","author":"Hudjakov","year":"2011","journal-title":"Estonian J. Eng."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"3144","DOI":"10.1080\/01431161.2015.1054049","article-title":"Road network extraction: A neural-dynamic framework based on deep learning and a finite state machine","volume":"36","author":"Wang","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1797","DOI":"10.1109\/LGRS.2014.2309695","article-title":"Vehicle detection in satellite images by hybrid deep convolutional neural networks","volume":"11","author":"Chen","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Ishii, T., Nakamura, R., Nakada, H., Mochizuki, Y., and Ishikawa, H. (2015, January 18\u201322). Surface object recognition with CNN and SVM in Landsat 8 images. Proceedings of the IEEE 2015 14th IAPR International Conference on Machine Vision Applications (MVA), Tokyo, Japan.","DOI":"10.1109\/MVA.2015.7153200"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1793","DOI":"10.1109\/TGRS.2015.2488681","article-title":"Scene classification via a gradient boosting random convolutional network framework","volume":"54","author":"Zhang","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","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_32","doi-asserted-by":"crossref","first-page":"905","DOI":"10.1080\/01431160701311309","article-title":"Object-oriented classification of sidescan sonar data for mapping benthic marine habitats","volume":"29","author":"Lucieer","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_33","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_34","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.rse.2011.07.020","article-title":"Assessment of spectral, polarimetric, temporal, and spatial dimensions for urban and peri-urban land cover classification using Landsat and SAR data","volume":"117","author":"Zhu","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Plaza, A., Plaza, J., and Martin, G. (2009, January 1\u20134). Incorporation of spatial constraints into spectral mixture analysis of remotely sensed hyperspectral data. Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing, Grenoble, France.","DOI":"10.1109\/MLSP.2009.5306202"},{"key":"ref_36","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_37","doi-asserted-by":"crossref","first-page":"3804","DOI":"10.1109\/TGRS.2008.922034","article-title":"Spectral and spatial classification of hyperspectral data using SVMs and morphological profiles","volume":"46","author":"Fauvel","year":"2008","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Midhun, M., Nair, S.R., Prabhakar, V., and Kumar, S.S. (2014, January 10\u201311). Deep model for classification of hyperspectral image using restricted boltzmann machine. Proceedings of the 2014 International Conference on Interdisciplinary Advances in Applied Computing (ACM), Amritapuri, India.","DOI":"10.1145\/2660859.2660946"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2381","DOI":"10.1109\/JSTARS.2015.2388577","article-title":"Spectral\u2014Spatial 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_40","doi-asserted-by":"crossref","unstructured":"Li, T., Zhang, J., and Zhang, Y. (2014, January 27\u201330). Classification of hyperspectral image based on deep belief networks. Proceedings of the 2014 IEEE International Conference on Image Processing (ICIP), Paris, France.","DOI":"10.1109\/ICIP.2014.7026039"},{"key":"ref_41","unstructured":"Mnih, V., and Hinton, G.E. (2010). Computer Vision\u2013ECCV 2010, Springer."},{"key":"ref_42","unstructured":"Boggess, J.E. (1993). Identification of Roads in Satellite Imagery Using Artificial Neural Networks: A Contextual Approach, Mississippi State University."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1798","DOI":"10.1109\/TPAMI.2013.50","article-title":"Representation learning: A review and new perspectives","volume":"35","author":"Bengio","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1109\/TASL.2011.2134090","article-title":"Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition","volume":"20","author":"Dahl","year":"2012","journal-title":"IEEE Trans. Audio Speech Lang. Process."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Graves, A., Mohamed, A., and Hinton, G. (2013, January 26\u201330). Speech recognition with deep recurrent neural networks. Proceedings of the 38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Vancouver, BC, Canada.","DOI":"10.1109\/ICASSP.2013.6638947"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.patrec.2014.01.008","article-title":"A review of unsupervised feature learning and deep learning for time-series modeling","volume":"42","author":"Karlsson","year":"2014","journal-title":"Pattern Recognit. Lett."},{"key":"ref_47","unstructured":"Vricon, Homepage. Available online: http:\/\/www.vricon.com."},{"key":"ref_48","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_49","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_50","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_51","unstructured":"Sanchez, C., Gladstone, C., and Holland, D. (2007, January 11\u201313). Classification of urban features from Intergraph\u2019s Z\/I Imaging DMC high resolution images for integration into a change detection flowline within Ordnance Survey. Proceedings of the 2007 Urban Remote Sensing Joint Event, Paris, France."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"963","DOI":"10.14358\/PERS.69.9.963","article-title":"A comparison of urban mapping methods using high-resolution digital imagery","volume":"69","author":"Thomas","year":"2003","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_53","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_54","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_55","unstructured":"Lee, H., Largman, Y., Pham, P., and Ng, A.Y. (2009). Advances in Neural Information Processing Systems 22, Curran Associates."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Taylor, G., Fergus, R., LeCun, Y., and Bregler, C. (2010, January 5\u201311). Convolutional learning of spatio-temporal features. Proceedings of the European Conference on Computer Vision (ECCV\u201910), Crete, Greece.","DOI":"10.1007\/978-3-642-15567-3_11"},{"key":"ref_57","unstructured":"Jarrett, K., Kavukcuoglu, K., Ranzato, M., and LeCun, Y. (October, January 29). What is the best multi-stage architecture for object recognition?. Proceedings of the IEEE 12th International Conference on Computer Vision, Kyoto, Japan."},{"key":"ref_58","unstructured":"Nair, V., and Hinton, G.E. (2010, January 21\u201325). Rectified linear units improve restricted boltzmann machines. Proceedings of the 27th International Conference on Machine Learning (ICML-10), Haifa, Israel."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Vincent, P., Larochelle, H., Bengio, Y., and Manzagol, P.A. (2008, January 5\u20139). Extracting and composing robust features with denoising autoencoders. Proceedings of the 25th International Conference on Machine Learning, Helsinki, Finland.","DOI":"10.1145\/1390156.1390294"},{"key":"ref_60","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_61","unstructured":"Glorot, X., Bordes, A., and Bengio, Y. (2011, January 11\u201313). Deep sparse rectifier neural networks. Proceedings of the International Conference on Artificial Intelligence and Statistics, Fort Lauderdale, FL, USA."},{"key":"ref_62","unstructured":"Hu, F., Xia, G.S., Wang, Z., Zhang, L., and Sun, H. (2014, January 13\u201318). Unsupervised feature coding on local patch manifold for satellite image scene classification. Proceedings of the 2014 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Quebec City, QC, Canada."},{"key":"ref_63","first-page":"215","article-title":"An analysis of single-layer networks in unsupervised feature learning","volume":"15","author":"Coates","year":"2011","journal-title":"Engineering"},{"key":"ref_64","unstructured":"Pinheiro, P., and Collobert, R. (2014, January 21\u201326). Recurrent convolutional neural networks for scene labeling. Proceedings of the 31st International Conference on Machine Learning, Beijing, China."},{"key":"ref_65","unstructured":"Farabet, C., Couprie, C., Najman, L., and LeCun, Y. (July, January 26). Scene parsing with multiscale feature learning, purity trees, and optimal covers. Proceedings of the 29th International Conference on Machine Learning (ICML), Edinburgh, UK."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"2474","DOI":"10.3390\/rs70302474","article-title":"A region-based genesis segmentation algorithm for the classification of remotely sensed images","volume":"7","author":"Mylonas","year":"2015","journal-title":"Remote Sens."},{"key":"ref_67","unstructured":"Ester, M., Kriegel, H.P., Sander, J., and Xu, X. (1996). A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases With Noise, AAAI Press."},{"key":"ref_68","first-page":"1157","article-title":"An introduction to variable and feature selection","volume":"3","author":"Guyon","year":"2003","journal-title":"J. Mach. Learn. Res."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"2631","DOI":"10.1109\/36.803411","article-title":"A joint band prioritization and band-decorrelation approach to band selection for hyperspectral image classification","volume":"37","author":"Chang","year":"1999","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1360","DOI":"10.1109\/36.934069","article-title":"A new search algorithm for feature selection in hyperspectral remote sensing images","volume":"39","author":"Serpico","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"139","DOI":"10.5589\/m12-022","article-title":"Simultaneous feature selection and SVM parameter determination in classification of hyperspectral imagery using Ant Colony Optimization","volume":"38","author":"Samadzadegan","year":"2012","journal-title":"Can. J. Remote Sens."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/S0004-3702(97)00043-X","article-title":"Wrappers for feature subset selection","volume":"97","author":"Kohavi","year":"1997","journal-title":"Artif. Intell."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"4439","DOI":"10.1080\/01431160110114952","article-title":"Joint analysis of SAR, LiDAR and aerial imagery for simultaneous extraction of land cover, DTM and 3D shape of buildings","volume":"23","author":"Gamba","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_74","unstructured":"Martin L\u00e4ngkvist Academic Website. Available online: http:\/\/aass.oru.se\/mlt\/cnncode.zip."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/8\/4\/329\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T19:22:15Z","timestamp":1760210535000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/8\/4\/329"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,4,14]]},"references-count":74,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2016,4]]}},"alternative-id":["rs8040329"],"URL":"https:\/\/doi.org\/10.3390\/rs8040329","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,4,14]]}}}