{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T13:59:59Z","timestamp":1762955999704,"version":"build-2065373602"},"reference-count":47,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2018,12,17]],"date-time":"2018-12-17T00:00:00Z","timestamp":1545004800000},"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>Land cover and its dynamic information is the basis for characterizing surface conditions, supporting land resource management and optimization, and assessing the impacts of climate change and human activities. In land cover information extraction, the traditional convolutional neural network (CNN) method has several problems, such as the inability to be applied to multispectral and hyperspectral satellite imagery, the weak generalization ability of the model and the difficulty of automating the construction of a training database. To solve these problems, this study proposes a new type of deep convolutional neural network based on Landsat-8 Operational Land Imager (OLI) imagery. The network integrates cascaded cross-channel parametric pooling and average pooling layer, applies a hierarchical sampling strategy to realize the automatic construction of the training dataset, determines the technical scheme of model-related parameters, and finally performs the automatic classification of remote sensing images. This study used the new type of deep convolutional neural network to extract land cover information from Qinhuangdao City, Hebei Province, and compared the experimental results with those obtained by traditional methods. The results show that: (1) The proposed deep convolutional neural network (DCNN) model can automatically construct the training dataset and classify images. This model performs the classification of multispectral and hyperspectral satellite images using deep neural networks, which improves the generalization ability of the model and simplifies the application of the model. (2) The proposed DCNN model provides the best classification results in the Qinhuangdao area. The overall accuracy of the land cover data obtained is 82.0%, and the kappa coefficient is 0.76. The overall accuracy is improved by 5% and 14% compared to the support vector machine method and the maximum likelihood classification method, respectively.<\/jats:p>","DOI":"10.3390\/rs10122053","type":"journal-article","created":{"date-parts":[[2018,12,18]],"date-time":"2018-12-18T02:15:59Z","timestamp":1545099359000},"page":"2053","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":61,"title":["A Deep Convolution Neural Network Method for Land Cover Mapping: A Case Study of Qinhuangdao, China"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6219-6251","authenticated-orcid":false,"given":"Yunfeng","family":"Hu","sequence":"first","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Qianli","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Yunzhi","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Huimin","family":"Yan","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]}],"member":"1968","published-online":{"date-parts":[[2018,12,17]]},"reference":[{"key":"ref_1","first-page":"68","article-title":"A sub-pixel analysis of urbanization effect on land surface temperature and its interplay with impervious surface and vegetation coverage in Indianapolis, united states","volume":"10","author":"Weng","year":"2008","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1890\/1540-9295(2007)5[80:SHIUER]2.0.CO;2","article-title":"Spatial heterogeneity in urban ecosystems: Reconceptualizing land cover and a framework for classification","volume":"5","author":"Cadenasso","year":"2007","journal-title":"Front. Ecol. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.compenvurbsys.2012.06.003","article-title":"A review of regional science applications of satellite remote sensing in urban settings","volume":"37","author":"Patino","year":"2013","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1007\/s11442-014-1082-6","article-title":"Spatiotemporal characteristics, patterns, and causes of land-use changes in China since the late 1980s","volume":"24","author":"Liu","year":"2014","journal-title":"J. Geogr. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"5113","DOI":"10.1002\/2016JD025448","article-title":"Atmospheric, radiative, and hydrologic effects of future land use and land cover changes: A global and multimodel climate picture","volume":"122","author":"Quesada","year":"2017","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1093","DOI":"10.1080\/014311600210092","article-title":"Land cover mapping of large areas from satellites: Status and research priorities","volume":"21","author":"Cihlar","year":"2000","journal-title":"Int. J. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.isprsjprs.2014.09.002","article-title":"Global land cover mapping at 30 m resolution: A pok-based operational approach","volume":"103","author":"Chen","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Hu, Y., and Nacun, B. (2018). An analysis of land-use change and grassland degradation from a policy perspective in inner mongolia, China, 1990\u20132015. Sustainability, 10.","DOI":"10.3390\/su10114048"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1595","DOI":"10.1002\/ldr.2948","article-title":"Land-use change and land degradation on the mongolian plateau from 1975 to 2015\u2014A case study from Xilingol, China","volume":"29","author":"Claas","year":"2018","journal-title":"Land Degrad. Dev."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"823","DOI":"10.1080\/014311698215748","article-title":"Review article multisensor image fusion in remote sensing: Concepts, methods and applications","volume":"19","author":"Pohl","year":"1998","journal-title":"Int. J. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"785","DOI":"10.1080\/13658810110074483","article-title":"Integration of multi-source remote sensing data for land cover change detection","volume":"15","author":"Petit","year":"2001","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"4232","DOI":"10.1109\/TIP.2012.2199127","article-title":"Sar-based terrain classification using weakly supervised hierarchical markov aspect models","volume":"21","author":"Yang","year":"2012","journal-title":"IEEE Trans. Image Process."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3624","DOI":"10.3390\/rs6053624","article-title":"Classifying complex mountainous forests with l-band sar and landsat data integration: A comparison among different machine learning methods in the Hyrcanian forest","volume":"6","author":"Attarchi","year":"2014","journal-title":"Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.isprsjprs.2016.03.008","article-title":"Optical remotely sensed time series data for land cover classification: A review","volume":"116","author":"White","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1016\/j.isprsjprs.2018.10.008","article-title":"An automatic approach for land-change detection and land updates based on integrated NDVI timing analysis and the CVAPS method with GEE support","volume":"146","author":"Hu","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_16","first-page":"1","article-title":"A review of approaches to land use changes modeling","volume":"28","author":"Noszczyk","year":"2018","journal-title":"Hum. Ecol. Risk Assess."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1080\/15481603.2018.1426091","article-title":"Comparing fully convolutional networks, random forest, support vector machine, and patch-based deep convolutional neural networks for object-based wetland mapping using images from small unmanned aircraft system","volume":"55","author":"Liu","year":"2018","journal-title":"GISci. Remote Sens."},{"key":"ref_18","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_19","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":"2017","journal-title":"Pattern Recognit."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.knosys.2016.01.028","article-title":"Supervised remote sensing image segmentation using boosted convolutional neural networks","volume":"99","author":"Basaeed","year":"2016","journal-title":"Knowl. Based Syst."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.neucom.2016.09.010","article-title":"Convolutional neural networks for hyperspectral image classification","volume":"219","author":"Yu","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.rse.2018.04.050","article-title":"Urban land-use mapping using a deep convolutional neural network with high spatial resolution multispectral remote sensing imagery","volume":"214","author":"Huang","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_23","first-page":"26","article-title":"Responses of regional ecological service value to land use change\u2014A case study of Qinhuangdao city","volume":"1","author":"Zhang","year":"2010","journal-title":"J. Shanxi Normal Univ."},{"key":"ref_24","first-page":"9088","article-title":"Study on the ecological regionalization in qinhuangdao city based on gis graticule method","volume":"35","author":"Zhang","year":"2008","journal-title":"J. Anhui Agric. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.rse.2014.02.001","article-title":"Landsat-8: Science and product vision for terrestrial global change research","volume":"145","author":"Roy","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.rse.2011.08.024","article-title":"A review of large area monitoring of land cover change using landsat data","volume":"122","author":"Hansen","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"600","DOI":"10.3390\/rs70100600","article-title":"The ground-based absolute radiometric calibration of landsat 8 oli","volume":"7","author":"McCorkel","year":"2015","journal-title":"Remote Sens."},{"key":"ref_28","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_29","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.rse.2018.06.034","article-title":"An object-based convolutional neural network (OCNN) for urban land use classification","volume":"216","author":"Zhang","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_30","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_31","doi-asserted-by":"crossref","unstructured":"Zhou, H., Wang, Y., Lei, X., and Liu, Y. (2017, January 15\u201318). A Method of Improved CNN Traffic Classification. Proceedings of the 13th International Conference on Computational Intelligence and Security (CIS), Hong Kong, China.","DOI":"10.1109\/CIS.2017.00046"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Krenker, A., Be\u0161ter, J., and Kos, A. (2011). Introduction to the Artificial Neural Networks. Artificial Neural Networks: Methodological Advances and Biomedical Applications, Intech Open.","DOI":"10.5772\/15751"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Nguyen, A., Yosinski, J., and Clune, J. (2015). Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images, IEEE.","DOI":"10.1109\/CVPR.2015.7298640"},{"key":"ref_34","unstructured":"Lin, M., Chen, Q., and Yan, S. (arXiv, 2013). Network in network, arXiv."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Valada, A., Spinello, L., and Burgard, W. (2018). Deep Feature Learning for Acoustics-Based Terrain Classification, Springer.","DOI":"10.1007\/978-3-319-60916-4_2"},{"key":"ref_36","first-page":"1097","article-title":"Imagenet Classification with Deep Convolutional Neural Networks","volume":"20","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_37","unstructured":"Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R.R. (arXiv, 2012). Improving neural networks by preventing co-adaptation of feature detectors, arXiv."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., and Torralba, A. (2016). Learning Deep Features for Discriminative Localization, IEEE.","DOI":"10.1109\/CVPR.2016.319"},{"key":"ref_39","first-page":"566","article-title":"Improving neural networks with dropout","volume":"182","author":"Srivastava","year":"2013","journal-title":"Univ. Toronto"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1733","DOI":"10.1080\/0143116031000150077","article-title":"Comparison of maximum likelihood classification method with supervised artificial neural network algorithms for land use activities","volume":"25","author":"Erbek","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_41","first-page":"S27","article-title":"Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms","volume":"12","author":"Otukei","year":"2010","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2252","DOI":"10.1080\/01431161.2015.1035410","article-title":"A comparison of classification algorithms using landsat-7 and landsat-8 data for mapping lithology in canada\u2019s arctic","volume":"36","author":"He","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1250","DOI":"10.1016\/j.asr.2012.06.032","article-title":"Selection of classification techniques for land use\/land cover change investigation","volume":"50","author":"Srivastava","year":"2012","journal-title":"Adv. Space Res."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.isprsjprs.2017.11.021","article-title":"A new deep convolutional neural network for fast hyperspectral image classification","volume":"145","author":"Paoletti","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.isprsjprs.2018.01.021","article-title":"Land cover mapping at very high resolution with rotation equivariant cnns: Towards small yet accurate models","volume":"145","author":"Marcos","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1016\/j.isprsjprs.2018.08.005","article-title":"A 3d convolutional neural network method for land cover classification using lidar and multi-temporal landsat imagery","volume":"144","author":"Xu","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1016\/j.neunet.2018.05.019","article-title":"Land cover classification from multi-temporal, multi-spectral remotely sensed imagery using patch-based recurrent neural networks","volume":"105","author":"Sharma","year":"2018","journal-title":"Neural Netw."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/12\/2053\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:34:34Z","timestamp":1760196874000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/12\/2053"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,12,17]]},"references-count":47,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2018,12]]}},"alternative-id":["rs10122053"],"URL":"https:\/\/doi.org\/10.3390\/rs10122053","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2018,12,17]]}}}