{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T20:47:32Z","timestamp":1761598052769,"version":"build-2065373602"},"reference-count":74,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2019,3,14]],"date-time":"2019-03-14T00:00:00Z","timestamp":1552521600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2017YFA0603004"],"award-info":[{"award-number":["2017YFA0603004"]}]},{"name":"Science Foundation of Shandong","award":["ZR2017MD018","ZR2016DP04"],"award-info":[{"award-number":["ZR2017MD018","ZR2016DP04"]}]},{"DOI":"10.13039\/501100001809","name":"National Science Foundation of China","doi-asserted-by":"publisher","award":["41471299"],"award-info":[{"award-number":["41471299"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Open Research Project of the Key Laboratory for Meteorological Disaster Monitoring, Early Warning and Risk Management of Characteristic Agriculture in Arid Regions","award":["CAMF-201701","CAMF-201803"],"award-info":[{"award-number":["CAMF-201701","CAMF-201803"]}]},{"name":"Key Project of Shandong Provincial Meteorological Bureau","award":["2017sdqxz03"],"award-info":[{"award-number":["2017sdqxz03"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>When the spatial distribution of winter wheat is extracted from high-resolution remote sensing imagery using convolutional neural networks (CNN), field edge results are usually rough, resulting in lowered overall accuracy. This study proposed a new per-pixel classification model using CNN and Bayesian models (CNN-Bayesian model) for improved extraction accuracy. In this model, a feature extractor generates a feature vector for each pixel, an encoder transforms the feature vector of each pixel into a category-code vector, and a two-level classifier uses the difference between elements of category-probability vectors as the confidence value to perform per-pixel classifications. The first level is used to determine the category of a pixel with high confidence, and the second level is an improved Bayesian model used to determine the category of low-confidence pixels. The CNN-Bayesian model was trained and tested on Gaofen 2 satellite images. Compared to existing models, our approach produced an improvement in overall accuracy, the overall accuracy of SegNet, DeepLab, VGG-Ex, and CNN-Bayesian was 0.791, 0.852, 0.892, and 0.946, respectively. Thus, this approach can produce superior results when winter wheat spatial distribution is extracted from satellite imagery.<\/jats:p>","DOI":"10.3390\/rs11060619","type":"journal-article","created":{"date-parts":[[2019,3,15]],"date-time":"2019-03-15T04:12:09Z","timestamp":1552623129000},"page":"619","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["A New CNN-Bayesian Model for Extracting Improved Winter Wheat Spatial Distribution from GF-2 imagery"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9891-7762","authenticated-orcid":false,"given":"Chengming","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Information Science and Engineering, Shandong Agricultural University, 61 Daizong Road, Taian 271000, Shandong, China"},{"name":"Shandong Technology and Engineering Center for Digital Agriculture, 61 Daizong Road, Taian 271000, Shandong, China"}]},{"given":"Yingjuan","family":"Han","sequence":"additional","affiliation":[{"name":"Key Laboratory for Meteorological Disaster Monitoring and Early Warning and Risk Management of Characteristic Agriculture in Arid Regions, CMA, 71 Xinchangxi Road, Yinchuan 750002, Ningxia, China"}]},{"given":"Feng","family":"Li","sequence":"additional","affiliation":[{"name":"Shandong Provincal Climate Center, NO.12 Wuying Mountain Road, Jinan 250001, Shandong, China"}]},{"given":"Shuai","family":"Gao","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, 9 Dengzhuangnan Road, Beijing 100094, China"}]},{"given":"Dejuan","family":"Song","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Shandong Agricultural University, 61 Daizong Road, Taian 271000, Shandong, China"},{"name":"Shandong Technology and Engineering Center for Digital Agriculture, 61 Daizong Road, Taian 271000, Shandong, China"}]},{"given":"Hui","family":"Zhao","sequence":"additional","affiliation":[{"name":"Key Laboratory for Meteorological Disaster Monitoring and Early Warning and Risk Management of Characteristic Agriculture in Arid Regions, CMA, 71 Xinchangxi Road, Yinchuan 750002, Ningxia, China"}]},{"given":"Keqi","family":"Fan","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Shandong Agricultural University, 61 Daizong Road, Taian 271000, Shandong, China"}]},{"given":"Ya\u2019nan","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Shandong Agricultural University, 61 Daizong Road, Taian 271000, Shandong, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,3,14]]},"reference":[{"key":"ref_1","unstructured":"(2018, August 08). Websit of Food and Agriculture Organization of the United Nations. Available online: http:\/\/www.fao.org\/faostat\/zh\/#data\/QC."},{"key":"ref_2","unstructured":"(2017, December 08). Announcement of the National Statistics Bureau on Grain Output in 2017, Available online: http:\/\/www.gov.cn\/xinwen\/2017-12\/08\/content_5245284.htm."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.isprsjprs.2014.04.023","article-title":"Improved maize cultivated area estimation over a large scale combining MODIS\u2013EVI time series data and crop phenological information","volume":"94","author":"Zhang","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1016\/S2095-3119(16)61442-9","article-title":"Assessment of the cropland classifications in four global land cover datasets: A case study of Shaanxi Province, China","volume":"16","author":"Chen","year":"2017","journal-title":"J. Integnit. Agric."},{"key":"ref_5","first-page":"147","article-title":"Remote sensing measurement of corn planting area based on field-data","volume":"25","author":"Ma","year":"2009","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1016\/j.rse.2012.05.018","article-title":"High-frequency remote monitoring of large lakes with MODIS 500 m imagery","volume":"124","author":"McCullough","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_7","first-page":"168","article-title":"Study on scale issues in measurement of winter wheat plant area by remote sensing","volume":"12","author":"Hao","year":"2008","journal-title":"J. Remote Sens."},{"key":"ref_8","first-page":"184","article-title":"Area change monitoring of winter wheat based on relationship analysis of GF-1 NDVI among different years","volume":"34","author":"Wang","year":"2018","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Wang, D., Fang, S., Yang, Z., Wang, L., Tang, W., Li, Y., and Tong, C. (2018). A regional mapping method for oilseed rape based on HSV transformation and spectral features. ISPRS Int. J. Geo-Informat., 7.","DOI":"10.3390\/ijgi7060224"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"684","DOI":"10.1007\/s11119-017-9549-y","article-title":"Automatic delineation algorithm for site-specific management zones based on satellite remote sensingdata","volume":"19","author":"Georgi","year":"2018","journal-title":"Precis. Agric."},{"key":"ref_11","first-page":"146","article-title":"Classification method by fusion of decision tree and SVM based on Sentinel-2A image","volume":"49","author":"Wang","year":"2018","journal-title":"Trans. Chin. Soc. Agric. Mach."},{"key":"ref_12","first-page":"758","article-title":"Evaluation of the effect of feature extraction strategy on the performance of high-resolution remote sensing image scene classification","volume":"22","author":"Qian","year":"2018","journal-title":"J. Remote Sens."},{"key":"ref_13","first-page":"182","article-title":"Extraction of winter wheat planted area in Jiangsu province using decision tree and mixed-pixel methods","volume":"32","author":"Wang","year":"2016","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_14","first-page":"1808","article-title":"Extraction of main crops in Yellow River Delta based on MODIS NDVI time series","volume":"32","author":"Guo","year":"2017","journal-title":"J. Nat. Res."},{"key":"ref_15","first-page":"134","article-title":"Crop information identification based on MODIS NDVI time-series data","volume":"30","author":"Xu","year":"2014","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_16","first-page":"201","article-title":"Crop planting extraction based on multi-temporal remote sensing data in Northeast China","volume":"27","author":"Hao","year":"2011","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_17","first-page":"103","article-title":"Monitoring planting area and growth situation of irrigation-land and dry-land winter wheat based on TM and MODIS data","volume":"25","author":"Feng","year":"2009","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_18","first-page":"150","article-title":"Extracting winter wheat area in Huanghuaihai Plain using MODIS-EVI data and phenology difference avoiding threshold","volume":"34","author":"Sha","year":"2018","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1016\/j.neucom.2016.02.061","article-title":"Feature extraction using dual-tree complex wavelet transform and gray level co-occurrence matrix","volume":"197","author":"Yang","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"652","DOI":"10.1016\/j.isprsjprs.2011.04.006","article-title":"Identification of hazelnut fields using spectral and Gabor textural features","volume":"66","author":"Reis","year":"2011","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1016\/j.jseaes.2017.07.031","article-title":"Detection of cretaceous incised-valley shale for resource play, Miano gas field, SW Pakistan: Spectral decomposition using continuous wavelet transform","volume":"147","author":"Naseera","year":"2017","journal-title":"J Asian. Earth. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.isprsjprs.2012.01.007","article-title":"Discrepancy measures for selecting optimal combination of parameter values in object-based image analysis","volume":"68","author":"Liu","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.isprsjprs.2013.09.014","article-title":"Geographic Object-Based Image Analysis\u2014Towards a new paradigm","volume":"87","author":"Blaschke","year":"2013","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_24","first-page":"146","article-title":"Mapping crops acreages based on remote sensing and sampling investigation by multivariate probability proportional to size","volume":"30","author":"Wu","year":"2014","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_25","first-page":"131","article-title":"Area extraction of winter wheat at county scale based on modified multivariate texture and GF-1 satellite images","volume":"32","author":"You","year":"2016","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"0428001","DOI":"10.3788\/AOS201636.0428001","article-title":"High spatial resolution remote sensing image classification based on deep learning","volume":"36","author":"Liu","year":"2016","journal-title":"Acta Opt. Sin."},{"key":"ref_27","first-page":"1211","article-title":"Automatic analysis and mining of remote sensing big data","volume":"43","author":"Li","year":"2014","journal-title":"J. Surv. Mapp."},{"key":"ref_28","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":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1276","DOI":"10.1016\/j.rse.2009.02.014","article-title":"A neural network approach using multi-scale textural metrics from very high-resolution panchromatic imagery for urban land-use classification","volume":"113","author":"Pacifici","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_30","first-page":"228","article-title":"Fusion of pixel-based and multi-scale region-based features for the classification of high-resolution remote sensing image","volume":"5","author":"Liu","year":"2015","journal-title":"J. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1109\/TGRS.2012.2202912","article-title":"An SVM ensemble approach combining spectral, structural, and semantic features for the classification of high-resolution remotely sensed imagery","volume":"51","author":"Huang","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","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_33","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_35","unstructured":"Simonyan, K., and Zisserman, A. (arXiv, 2014). Very deep convolutional networks for large-scale image recognition, arXiv."},{"key":"ref_36","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_37","first-page":"1300","article-title":"Convolutional neural networks in image understanding","volume":"42","author":"Chang","year":"2016","journal-title":"Acta Autom. Sin."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Fischer, W., Moudgalya, S.S., Cohn, J.D., Nguyen, N.T.T., and Kenyon, G.T. (2018). Sparse coding of pathology slides compared to transfer learning with deep neural networks. BMC Bioinform., 19.","DOI":"10.1186\/s12859-018-2504-8"},{"key":"ref_39","first-page":"640","article-title":"Fully convolutional networks for semantic segmentation","volume":"39","author":"Long","year":"2014","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_40","unstructured":"Badrinarayanan, V., Kendall, A., and Cipolla, R. (arXiv, 2015). SegNet: A deep convolutional encoder-decoder architecture for image segmentation, arXiv."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (arXiv, 2015). U-Net: Convolutional networks for biomedical image segmentation, arXiv.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_42","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":"2018","journal-title":"IEEE Trans. Patt. Anal. Mach. Intell."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Visin, F., Romero, A., Cho, K., Matteucci, M., and Courville, A. (arXiv, 2016). ReSeg: A recurrent neural network-based model for semantic segmentation, arXiv.","DOI":"10.1109\/CVPRW.2016.60"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1016\/j.neucom.2018.08.052","article-title":"Semantic scene completion with dense CRF from a single depth image","volume":"318","author":"Zhang","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_45","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_46","doi-asserted-by":"crossref","unstructured":"Fu, K., Lu, W., Diao, W., Yan, M., Sun, H., Zhang, Y., and Sun, X. (2018). WSF-NET: Weakly supervised feature-fusion network for binary segmentation in remote sensing image. Remote Sens., 10.","DOI":"10.3390\/rs10121970"},{"key":"ref_47","unstructured":"Castelluccio, M., Poggi, G., Sansone, C., and Verdoliva, L. (arXiv, 2015). Land use classification in remote sensing images by convolutional neural networks, arXiv."},{"key":"ref_48","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_49","doi-asserted-by":"crossref","unstructured":"Lin, H., Shi, Z., and Zou, Z. (2017). Maritime semantic labeling of optical remote sensing images with multi-scale fully convolutional network. Remote Sens., 9.","DOI":"10.3390\/rs9050480"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.neunet.2017.07.017","article-title":"A patch-based convolutional neural network for remote sensing image classification","volume":"95","author":"Sharma","year":"2017","journal-title":"Neural Netw."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Gaetano, R., Ienco, D., Ose, K., and Cresson, R. (2018). A two-branch CNN architecture for land cover classification of PAN and MS imagery. Remote Sens., 10.","DOI":"10.3390\/rs10111746"},{"key":"ref_52","first-page":"202","article-title":"Field rice panicle segmentation based on deep full convolutional neural network","volume":"34","author":"Duan","year":"2018","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Jiang, T., Liu, X., and Wu, L. (2018). Method for mapping rice fields in complex landscape areas based on pre-trained convolutional neural network from HJ-1 A\/B data. ISPRS Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7110418"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1186\/s13007-018-0366-8","article-title":"Detection and analysis of wheat spikes using Convolutional Neural Networks","volume":"14","author":"Hasan","year":"2018","journal-title":"Plant Methods"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1186\/s13007-017-0245-8","article-title":"Acquiring and preprocessing leaf images for automated plant identification: Understanding the tradeoff between effort and information gain","volume":"13","author":"Rzanny","year":"2017","journal-title":"Plant Methods"},{"key":"ref_56","first-page":"186","article-title":"Remote sensing estimation of rape planting area based on improved AlexNet model","volume":"26","author":"Jiao","year":"2018","journal-title":"Comp. Meas. Cont."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Huang, H., Deng, J., Lan, Y., Yang, A., Deng, X., and Zhang, L. (2018). A fully convolutional network for weed mapping of unmanned aerial vehicle (UAV) imagery. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0196302"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Huang, H., Deng, J., Lan, Y., Yang, A., Deng, X., Wen, S., Zhang, H., and Zhang, Y. (2018). Accurate weed mapping and prescription map generation based on fully convolutional networks using UAV imagery. Sensors, 18.","DOI":"10.3390\/s18103299"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Huang, H., Lan, Y., Deng, J., Yang, A., Deng, X., Zhang, L., and Wen, S. (2018). A semantic labeling approach for accurate weed mapping of high resolution UAV imagery. Sensors, 18.","DOI":"10.3390\/s18072113"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"042621","DOI":"10.1117\/1.JRS.11.042621","article-title":"Deep convolutional neural network for classifying Fusarium wilt of radish from unmanned aerial vehicles","volume":"11","author":"Ha","year":"2017","journal-title":"J Appl. Remote Sens."},{"key":"ref_61","first-page":"194","article-title":"Image recognition of Camellia oleifera diseases based on convolutional neural network & transfer learning","volume":"34","author":"Long","year":"2018","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_62","first-page":"78","article-title":"Detecting grape diseases based on convolutional neural network","volume":"49","author":"Liu","year":"2018","journal-title":"J. Northeast. Agric. Univ."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1109\/TITS.2017.2749964","article-title":"Embedding Structured Contour and Location Prior in Siamesed Fully Convolutional Networks for Road Detection","volume":"19","author":"Wang","year":"2018","journal-title":"IEEE Trans. Intell. Transp."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Ji, S., Zhang, C., Xu, A., Shi, Y., and Duan, Y. (2018). 3D convolutional neural networks for crop classification with multi-temporal remote sensing images. Remote Sens., 10.","DOI":"10.3390\/rs10010075"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Ndikumana, E., Ho Tong Minh, D., Baghdadi, N., Courault, D., and Hossard, L. (2018). Deep recurrent neural network for agricultural classification using multitemporal SAR Sentinel-1 for Camargue, France. Remote Sens., 10.","DOI":"10.1117\/12.2325160"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1186\/s13007-018-0333-4","article-title":"Deep phenotyping: Deep learning for temporal phenotype\/genotype classification","volume":"14","author":"Namin","year":"2018","journal-title":"Plant Methods"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Maggiori, E., Charpiat, G., Tarabalka, Y., and Alliez, P. (arXiv, 2017). Recurrent Neural Networks to Correct Satellite Image Classification Maps, arXiv.","DOI":"10.1109\/TGRS.2017.2697453"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"5076","DOI":"10.1109\/TIP.2018.2848470","article-title":"Structured AutoEncoders for Subspace Clustering","volume":"27","author":"Peng","year":"2018","journal-title":"IEEE T Image Process"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"2077","DOI":"10.1109\/LGRS.2017.2751559","article-title":"Locality Adaptive Discriminant Analysis for Spectral-Spatial Classification of Hyperspectral Images","volume":"14","author":"Wang","year":"2017","journal-title":"IEEE Geosci. Remote Sens."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Huang, Z., Zhu, H., Zhou, J.T., and Peng, X. (2018). Multiple Marginal Fisher Analysis. IEEE Trans. Ind. Electron.","DOI":"10.1109\/TIE.2018.2870413"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Jung, M.C., Park, J., and Kim, S. (2019). Spatial Relationships between Urban Structures and Air Pollution in Korea. Sustainability, 11.","DOI":"10.3390\/su11020476"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"935","DOI":"10.5194\/amt-12-935-2019","article-title":"Improving the mean and uncertainty of ultraviolet multi-filter rotating shadowband radiometer in situ calibration factors: Utilizing Gaussian process regression with a new method to estimate dynamic input uncertainty","volume":"12","author":"Chen","year":"2019","journal-title":"Atmos. Meas. Tech."},{"key":"ref_73","unstructured":"(2018, October 21). Website of Zhangqiu County People\u2019s Government, Available online: http:\/\/www.jnzq.gov.cn\/col\/col22490\/index.html."},{"key":"ref_74","unstructured":"(2018, May 29). Calibration Parameters for Part of Chinese Satellite Images. Available online: http:\/\/www.cresda.com\/CN\/Downloads\/dbcs\/index.shtml."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/6\/619\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:38:41Z","timestamp":1760186321000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/6\/619"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,3,14]]},"references-count":74,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2019,3]]}},"alternative-id":["rs11060619"],"URL":"https:\/\/doi.org\/10.3390\/rs11060619","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2019,3,14]]}}}