{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T03:41:56Z","timestamp":1768707716197,"version":"3.49.0"},"reference-count":63,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2018,3,14]],"date-time":"2018-03-14T00:00:00Z","timestamp":1520985600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Considering the classification of high spatial resolution remote sensing imagery, this paper presents a novel classification method for such imagery using deep neural networks. Deep learning methods, such as a fully convolutional network (FCN) model, achieve state-of-the-art performance in natural image semantic segmentation when provided with large-scale datasets and respective labels. To use data efficiently in the training stage, we first pre-segment training images and their labels into small patches as supplements of training data using graph-based segmentation and the selective search method. Subsequently, FCN with atrous convolution is used to perform pixel-wise classification. In the testing stage, post-processing with fully connected conditional random fields (CRFs) is used to refine results. Extensive experiments based on the Vaihingen dataset demonstrate that our method performs better than the reference state-of-the-art networks when applied to high-resolution remote sensing imagery classification.<\/jats:p>","DOI":"10.3390\/ijgi7030110","type":"journal-article","created":{"date-parts":[[2018,3,15]],"date-time":"2018-03-15T05:06:43Z","timestamp":1521090403000},"page":"110","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":49,"title":["Pixel-Wise Classification Method for High Resolution Remote Sensing Imagery Using Deep Neural Networks"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0659-4025","authenticated-orcid":false,"given":"Rui","family":"Guo","sequence":"first","affiliation":[{"name":"Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Haidian District, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, No. 19 (A) Yuquan Road, Shijingshan District, Beijing 100049, China"}]},{"given":"Jianbo","family":"Liu","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Haidian District, Beijing 100094, China"}]},{"given":"Na","family":"Li","sequence":"additional","affiliation":[{"name":"School of Econometrics and Management, University of the Chinese Academy of Sciences, No.19 (A) Yuquan Road, Shijingshan District, Beijing 100049, China"}]},{"given":"Shibin","family":"Liu","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Haidian District, Beijing 100094, China"}]},{"given":"Fu","family":"Chen","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Haidian District, Beijing 100094, China"}]},{"given":"Bo","family":"Cheng","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Haidian District, Beijing 100094, China"}]},{"given":"Jianbo","family":"Duan","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Haidian District, Beijing 100094, China"}]},{"given":"Xinpeng","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Haidian District, Beijing 100094, China"}]},{"given":"Caihong","family":"Ma","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Haidian District, Beijing 100094, China"}]}],"member":"1968","published-online":{"date-parts":[[2018,3,14]]},"reference":[{"key":"ref_1","unstructured":"MacQueen, J. (July, January 21). Some Methods for classification and Analysis of Multivariate Observations. Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, University of California, Berkeley, CA, USA."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1016\/0098-3004(94)00082-6","article-title":"Neural network classification of remote-sensing data","volume":"21","author":"Miller","year":"1995","journal-title":"Comput. Geosci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"617","DOI":"10.1080\/01431160701352154","article-title":"The application of artificial neural networks to the analysis of remotely sensed data","volume":"29","author":"Mas","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1351","DOI":"10.1109\/TGRS.2005.846154","article-title":"Kernel-based methods for hyperspectral image classification","volume":"43","author":"Bruzzone","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.isprsjprs.2010.11.001","article-title":"Support vector machines in remote sensing: A review","volume":"66","author":"Mountrakis","year":"2011","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1080\/01431160412331269698","article-title":"Random forest classifier for remote sensing classification","volume":"26","author":"Pal","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","article-title":"Random forest in remote sensing: A review of applications and future directions","volume":"114","author":"Belgiu","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Sturgess, P., Alahari, K., Ladicky, L., and Torr, P.H.S. (2009, January 7\u201310). Combining appearance and structure from motion features for road scene understanding. Proceedings of the British Machine Vision Conference, London, UK.","DOI":"10.5244\/C.23.62"},{"key":"ref_9","unstructured":"Definients Image (2004). eCognition User\u2019s Guide 4, Definients Image."},{"key":"ref_10","unstructured":"(2008). Feature Extraction Module Version 4.6. ENVI Feature Extraction Module User\u2019s Guide, ITT Corporation."},{"key":"ref_11","first-page":"1150","article-title":"Object Recognition from Local Scale-Invariant Features","volume":"2","author":"Lowe","year":"1999","journal-title":"Proc. Int. Conf. Comput. Vis."},{"key":"ref_12","first-page":"886","article-title":"Histograms of Oriented Gradients for Human Detection","volume":"1","author":"Dalal","year":"2005","journal-title":"IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Bottou, L. (2010, January 22\u201327). Large-scale machine learning with stochastic gradient descent. Proceedings of the COMPSTAT\u20192010, Paris, France.","DOI":"10.1007\/978-3-7908-2604-3_16"},{"key":"ref_14","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 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1007\/s11263-014-0733-5","article-title":"The pascal visual object classes challenge: A retrospective","volume":"111","author":"Everingham","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"ref_16","unstructured":"Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., and Yuille, A.L. (arXiv, 2016). DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs, arXiv."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Maggiori, E., Tarabalka, Y., Charpiat, G., and Alliez, P. (arXiv, 2016). High-resolution semantic labeling with convolutional neural networks, arXiv.","DOI":"10.1109\/IGARSS.2017.8128163"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Maggiori, E., Tarabalka, Y., Charpiat, G., and Alliez, P. (2017, January 23\u201328). Can Semantic Labeling Methods Generalize to Any City? The Inria Aerial Image Labeling Benchmark. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Fort Worth, TX, USA.","DOI":"10.1109\/IGARSS.2017.8127684"},{"key":"ref_19","first-page":"725","article-title":"Unsupervised Discovery of Nonlinear Structure Using Contrastive Backpropagation","volume":"30","author":"Hinton","year":"2006","journal-title":"Science"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","article-title":"A Fast Learning Algorithm for Deep Belief Nets","volume":"18","author":"Hinton","year":"2006","journal-title":"Neural Comput."},{"key":"ref_21","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_22","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":"Proc. IEEE"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"855","DOI":"10.1109\/TPAMI.2008.137","article-title":"A Novel Connectionist System for Improved Unconstrained Handwriting Recognition","volume":"31","author":"Graves","year":"2009","journal-title":"IEEE Trans. Pattern Anal."},{"key":"ref_24","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G. (2013, January 5\u201310). Imagenet classification with deep convolutional neural networks. Proceedings of the Neural Information Processing Systems, Lake Tahoe, CA, USA."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Ciresan, D., Meier, U., and Schmidhuber, J. (2012, January 16\u201321). Multi-column deep neural networks for image classification. Proceedings of the Conference on Computer Vision and Pattern Recognition, Providence, RI, USA.","DOI":"10.1109\/CVPR.2012.6248110"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollar, P., and Zitnick, C.L. (2014, January 6\u201312). Microsoft coco: Common objects in context. Proceedings of the European Conference on Computer Vision ECCV, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref_27","unstructured":"Vijay, B., Kendall, A., and Cipolla, R. (arXiv, 2015). SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation, arXiv."},{"key":"ref_28","unstructured":"Simonyan, K., and Zisserman, A. (2015, January 7\u20139). Very Deep Convolutional Networks for Large-Scale Image Recognition. Proceedings of the 3rd International Conference on Learning Representations, San Diego, CA, USA."},{"key":"ref_29","unstructured":"Yu, F., and Koltun, V. (arXiv, 2015). Multi-Scale Context Aggregation by Dilated Convolutions, arXiv."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zheng, S., Jayasumana, S., Romera-Paredes, B., Vineet, V., Su, Z., Du, D., Huang, C., and Torr, P.H.S. (arXiv, 2015). Conditional Random Fields as Recurrent Neural Networks, arXiv.","DOI":"10.1109\/ICCV.2015.179"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1109\/72.279181","article-title":"Learning long-term dependencies with gradient descent is difficult","volume":"5","author":"Bengio","year":"1994","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_32","unstructured":"Pascanu, R., Gulcehre, C., Cho, K., and Bengio, Y. (2013, January 16\u201321). On the difficulty of training recurrent neural networks. Proceedings of the International Conference on Machine Learning ICML, Atlanta, GA, USA."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Lin, G., Milan, A., Shen, C., and Reid, I. (arXiv, 2016). RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation, arXiv.","DOI":"10.1109\/CVPR.2017.549"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Peng, C., Zhang, X., Yu, G., Luo, G., and Sun, J. (arXiv, 2017). Large Kernel Matters\u2014Improve Semantic Segmentation by Global Convolutional Network, arXiv.","DOI":"10.1109\/CVPR.2017.189"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J. (arXiv, 2016). Pyramid Scene Parsing Network, arXiv.","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref_36","unstructured":"Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., and Yuille, A.L. (arXiv, 2014). Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs, arXiv."},{"key":"ref_37","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":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Mnih, V., and Hinton, G. (2010, January 5\u201311). Learning to Detect Roads in High-Resolution Aerial Images. Proceedings of the 11th European Conference on Computer Vision ECCV, Crete, Greece.","DOI":"10.1007\/978-3-642-15567-3_16"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Basu, S., Ganguly, S., Mukhopadhyay, S., DiBiano, R., Karki, M., and Nemani, R. (2015, January 3\u20136). DeepSat\u2014A Learning framework for Satellite Imagery. Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems, Seattle, WA, USA.","DOI":"10.1145\/2820783.2820816"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"5690","DOI":"10.1109\/TGRS.2015.2428197","article-title":"A Semiautomated Probabilistic Framework for Tree-Cover Delineation from 1-m NAIP Imagery Using a High-Performance Computing Architecture","volume":"53","author":"Basu","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Penatti, O.A.B., 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 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPRW.2015.7301382"},{"key":"ref_42","unstructured":"Mnih, V. (2013). Machine Learning for Aerial Image Labeling. [Ph.D. Thesis, University of Toronto]."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"881","DOI":"10.1109\/TGRS.2016.2616585","article-title":"Dense semantic labeling of sub-decimeter resolution images with convolutional neural networks","volume":"55","author":"Volpi","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sen."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Liu, Y., Nguyen, D., Deligiannis, N., Ding, W., and Munteanu, A. (2017). Hourglass-ShapeNetwork Based Semantic Segmentation for High Resolution Aerial Imagery. Remote Sens., 9.","DOI":"10.3390\/rs9060522"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"368","DOI":"10.3390\/rs9040368","article-title":"Segment-before-Detect: Vehicle Detection and Classification through Semantic Segmentation of Aerial Images","volume":"9","author":"Audebert","year":"2017","journal-title":"Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Fu, G., Liu, C., Zhou, R., Sun, T., and Zhang, Q. (2017). Classification for High Resolution Remote Sensing Imagery Using a Fully Convolutional Network. Remote Sens., 9.","DOI":"10.3390\/rs9050498"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Marmanis, D., Wegner, J.D., Galliani, S., Schindler, K., Datcu, M., and Stilla, U. (2016, January 12\u201319). Semantic segmentation of aerial images with an ensemble of CNNs. Proceedings of the ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Prague, Czech Republic.","DOI":"10.5194\/isprs-annals-III-3-473-2016"},{"key":"ref_48","unstructured":"Marmanis, D., Schindler, K., Wegner, J.D., Galliani, S., Datcu, M., and Stilla, U. (arXiv, 2016). Classification with an edge: Improving semantic image segmentation with boundary detection, arXiv."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Taylor, L., and Nitschke, G. (arXiv, 2017). Improving Deep Learning using Generic Data Augmentation, arXiv.","DOI":"10.1109\/SSCI.2018.8628742"},{"key":"ref_50","unstructured":"Zhang, C., Bengio, S., Hardt, M., Recht, B., and Vinyals, O. (arXiv, 2016). Understanding deep learning requires rethinking generalization, arXiv."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1007\/s11263-013-0620-5","article-title":"Selective search for object recognition","volume":"104","author":"Uijlings","year":"2013","journal-title":"Int. J. Comput. Vis."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Zitnick, C., and Doll\u00e1r, P. (2014, January 6\u201312). Edge boxes: Locating object proposals from edges. Proceedings of the European Conference on Computer Vision ECCV, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10602-1_26"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Salazar, A., Igual, J., Safont, G., Vergara, L., and Vidal, A. (2015, January 7\u20139). Image applications of agglomerative clustering using mixtures of non-Gaussian distributions. Proceedings of the 2015 International Conference on Computational Science and Computational Intelligence, CSCI, Las Vegas, NV, USA.","DOI":"10.1109\/CSCI.2015.118"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1023\/B:VISI.0000022288.19776.77","article-title":"Efficient Graph-Based Image Segmentation","volume":"59","author":"Felzenszwalb","year":"2004","journal-title":"Int. J. Comput. Vis."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TIP.2003.819861","article-title":"Image quality assessment: From error visibility to structural similarity","volume":"13","author":"Wang","year":"2004","journal-title":"IEEE Trans. Image Process."},{"key":"ref_56","unstructured":"Kr\u00e4henb\u00fchl, P., and Koltun, V. (2011, January 16\u201317). Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials. Proceedings of the Neural Information Processing Systems NIPS, Granada, Spain."},{"key":"ref_57","unstructured":"(2017, January 20). ISPRS (International Society for Photogrammetry and Remote Sensing). Available online: http:\/\/www2.isprs.org\/commissions\/comm3\/wg4\/semanticlabeling.html."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., and Darrell, T. (arXiv, 2014). Caffe: Convolutional architecture for fast feature embedding, arXiv.","DOI":"10.1145\/2647868.2654889"},{"key":"ref_59","unstructured":"(2017). GeForce GTX1080 Ti, NVIDIA Corporation."},{"key":"ref_60","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_61","doi-asserted-by":"crossref","unstructured":"He, K., and Sun, J. (2015, January 7\u201312). Convolutional Neural Networks at Constrained Time Cost. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7299173"},{"key":"ref_62","unstructured":"Paszke, A., Chaurasia, A., Kim, S., and Culurciello, E. (arXiv, 2016). ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation, arXiv."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.dsp.2015.11.009","article-title":"On the fusion of non-independent detectors","volume":"50","author":"Vergara","year":"2016","journal-title":"Digit. Signal Process."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/7\/3\/110\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T14:57:05Z","timestamp":1760194625000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/7\/3\/110"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,3,14]]},"references-count":63,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2018,3]]}},"alternative-id":["ijgi7030110"],"URL":"https:\/\/doi.org\/10.3390\/ijgi7030110","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,3,14]]}}}