{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T11:35:19Z","timestamp":1777894519569,"version":"3.51.4"},"reference-count":54,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2018,10,30]],"date-time":"2018-10-30T00:00:00Z","timestamp":1540857600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41701387"],"award-info":[{"award-number":["41701387"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Accurate and timely information about rice planting areas is essential for crop yield estimation, global climate change and agricultural resource management. In this study, we present a novel pixel-level classification approach that uses convolutional neural network (CNN) model to extract the features of enhanced vegetation index (EVI) time series curve for classification. The goal is to explore the practicability of deep learning techniques for rice recognition in complex landscape regions, where rice is easily confused with the surroundings, by using mid-resolution remote sensing images. A transfer learning strategy is utilized to fine tune a pre-trained CNN model and obtain the temporal features of the EVI curve. Support vector machine (SVM), a traditional machine learning approach, is also implemented in the experiment. Finally, we evaluate the accuracy of the two models. Results show that our model performs better than SVM, with the overall accuracies being 93.60% and 91.05%, respectively. Therefore, this technique is appropriate for estimating rice planting areas in southern China on the basis of a pre-trained CNN model by using time series data. And more opportunity and potential can be found for crop classification by remote sensing and deep learning technique in the future study.<\/jats:p>","DOI":"10.3390\/ijgi7110418","type":"journal-article","created":{"date-parts":[[2018,10,31]],"date-time":"2018-10-31T03:50:56Z","timestamp":1540957856000},"page":"418","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["Method for Mapping Rice Fields in Complex Landscape Areas Based on Pre-Trained Convolutional Neural Network from HJ-1 A\/B Data"],"prefix":"10.3390","volume":"7","author":[{"given":"Tian","family":"Jiang","sequence":"first","affiliation":[{"name":"School of Information Engineering, China University of Geosciences, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangnan","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Information Engineering, China University of Geosciences, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ling","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Information Engineering, China University of Geosciences, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,10,30]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"Rice and its importance to human life","volume":"8","author":"Gnanamanickam","year":"2009","journal-title":"Prog. Biol. Control"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1038\/514S50a","article-title":"Rice by the numbers: A good grain","volume":"514","author":"Elert","year":"2014","journal-title":"Nature"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1023\/A:1009894619446","article-title":"Using a crop\/soil simulation model and GIS techniques to assess methane emissions from rice fields in Asia. I. Model development","volume":"58","author":"Matthews","year":"2000","journal-title":"Nutr. Cycl. Agroecosyst"},{"key":"ref_4","unstructured":"FAOSTAT (1994\u20132014). Statistical Database of the Food and Agricultural Organization of the United Nations, FAO."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"11993","DOI":"10.1073\/pnas.202483599","article-title":"Photosynthate allocations in rice plants: Food production or atmospheric methane?","volume":"99","author":"Sass","year":"2002","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1029\/1999GB900096","article-title":"Changes in ch 4 emission from rice fields from 1960 to 1990s: 1. Impacts of modern rice technology","volume":"14","year":"2000","journal-title":"Glob. Biogeochem. Cycles"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"812","DOI":"10.1126\/science.1185383","article-title":"Food security: The challenge of feeding 9 billion people","volume":"327","author":"Godfray","year":"2010","journal-title":"Science"},{"key":"ref_8","first-page":"1346","article-title":"Methane emission, rice production and food security","volume":"93","author":"Seshadri","year":"2007","journal-title":"Curr. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"3019","DOI":"10.1080\/01431160310001619607","article-title":"Remote sensing and land cover area estimation","volume":"25","author":"Gallego","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"11518","DOI":"10.3390\/rs61111518","article-title":"Land cover classification of landsat data with phenological features extracted from time series modis NDVI data","volume":"6","author":"Jia","year":"2014","journal-title":"Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1080\/00207233.2010.535281","article-title":"Use of phenological features to identify cultivated areas in Asia","volume":"68","author":"Enkhzaya","year":"2011","journal-title":"Int. J. Environ. Stud."},{"key":"ref_12","first-page":"128","article-title":"Land cover classification of north China plain using MODIS_EVI temporal profile","volume":"22","author":"Xia","year":"2006","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3009","DOI":"10.1080\/01431160110107734","article-title":"Observation of flooding and rice transplanting of paddy rice fields at the site to landscape scales in China using vegetation sensor data","volume":"23","author":"Xiao","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Liao, J., Hu, Y., Zhang, H., Liu, L., Liu, Z., Tan, Z., and Wang, G. (2018). A rice mapping method based on time-series landsat data for the extraction of growth period characteristics. Sustainability, 10.","DOI":"10.3390\/su10072570"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1037","DOI":"10.1016\/j.mcm.2010.11.033","article-title":"Mapping rice planting areas in southern China using the China environment satellite data","volume":"54","author":"Chen","year":"2011","journal-title":"Math. Comput. Model."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"3833","DOI":"10.1016\/j.rse.2008.06.006","article-title":"Development of a two-band enhanced vegetation index without a blue band","volume":"112","author":"Jiang","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3467","DOI":"10.3390\/rs70403467","article-title":"Rice fields mapping in fragmented area using multi-temporal HJ-1A\/B CCD images","volume":"7","author":"Wang","year":"2015","journal-title":"Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1016\/j.rse.2004.12.009","article-title":"Mapping paddy rice agriculture in southern China using multi-temporal MODIS images","volume":"95","author":"Xiao","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1109\/TGRS.1995.8746029","article-title":"The interpretation of spectral vegetation indexes","volume":"33","author":"Myneni","year":"1995","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.rse.2005.10.004","article-title":"Mapping paddy rice agriculture in south and southeast Asia using multi-temporal MODIS images","volume":"100","author":"Xiao","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.rse.2016.02.016","article-title":"Mapping paddy rice planting area in northeastern Asia with landsat 8 images, phenology-based algorithm and google earth engine","volume":"185","author":"Dong","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1109\/TPWRD.2003.820178","article-title":"Disturbance classification utilizing dynamic time warping classifier","volume":"19","author":"Youssef","year":"2004","journal-title":"IEEE Trans. Power Deliv."},{"key":"ref_23","first-page":"731","article-title":"Dynamic time warp pattern matching using an integrated multiprocessing array","volume":"32","author":"Weste","year":"2006","journal-title":"IEEE Trans. Comput. C"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.cageo.2015.06.007","article-title":"The DTW-based representation space for seismic pattern classification","volume":"85","author":"Bicego","year":"2015","journal-title":"Comput. Geosci."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Guan, X., Huang, C., Liu, G., Meng, X., and Liu, Q. (2016). Mapping rice cropping systems in Vietnam using an NDVI-based time-series similarity measurement based on DTW distance. Remote Sens., 8.","DOI":"10.3390\/rs8010019"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2231","DOI":"10.1016\/j.patcog.2010.09.022","article-title":"Weighted dynamic time warping for time series classification","volume":"44","author":"Jeong","year":"2011","journal-title":"Pattern Recognit."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"3729","DOI":"10.1109\/JSTARS.2016.2517118","article-title":"A time-weighted dynamic time warping method for land-use and land-cover mapping","volume":"9","author":"Maus","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1016\/j.rse.2017.10.005","article-title":"Sentinel-2 cropland mapping using pixel-based and object-based time-weighted dynamic time warping analysis","volume":"204","author":"Belgiu","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/S0273-1177(01)00345-3","article-title":"Comparison of SAR and optical sensor data for monitoring of rice plant around hiroshima","volume":"28","author":"Oguro","year":"2001","journal-title":"Adv. Space Res."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"585","DOI":"10.1080\/01431160902894442","article-title":"Discriminating different landuse types by using multitemporal NDXI in a rice planting area","volume":"31","author":"Pan","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_31","unstructured":"Hong, S.Y., Lee, K.S., Rim, S.K., and Kim, K.U. (July, January 28). Estimation of rice field area using two-date landsat tm images in Korea. Proceedings of the Geoscience and Remote Sensing Symposium, Hamburg, Germany."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.rse.2015.01.004","article-title":"Tracking the dynamics of paddy rice planting area in 1986\u20132010 through time series landsat images and phenology-based algorithms","volume":"160","author":"Dong","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/S0034-4257(02)00096-2","article-title":"Overview of the radiometric and biophysical performance of the MODIS vegetation indices","volume":"83","author":"Huete","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"482","DOI":"10.1109\/36.142926","article-title":"Multispectral classification of landsat-images using neural networks","volume":"30","author":"Bischof","year":"1992","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.cageo.2011.08.019","article-title":"Support vector machines and object-based classification for obtaining land-use\/cover cartography from hyperion hyperspectral imagery","volume":"41","author":"Petropoulos","year":"2012","journal-title":"Comput. Geosci."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MGRS.2017.2762307","article-title":"Deep learning in remote sensing: A review","volume":"5","author":"Zhu","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_37","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_38","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_39","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.cageo.2018.05.012","article-title":"Detection of transverse cirrus bands in satellite imagery using deep learning","volume":"118","author":"Miller","year":"2018","journal-title":"Comput. Geosci."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.cageo.2016.12.015","article-title":"Automated detection of geological landforms on mars using convolutional neural networks","volume":"101","author":"Palafox","year":"2017","journal-title":"Comput. Geosci."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1186\/s40537-016-0043-6","article-title":"A survey of transfer learning","volume":"3","author":"Weiss","year":"2016","journal-title":"J. Big Data"},{"key":"ref_42","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_43","doi-asserted-by":"crossref","unstructured":"Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., and Darrell, T. (2014, January 3\u20137). Caffe: Convolutional architecture for fast feature embedding. Proceedings of the 22nd ACM international conference on Multimedia, Orlando, FL, USA.","DOI":"10.1145\/2647868.2654889"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2017.11.001","article-title":"Land cover classification and wetland inundation mapping using MODIS","volume":"204","author":"Vittorio","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/0034-4257(79)90013-0","article-title":"Red and photographic infrared linear combinations for monitoring vegetation","volume":"8","author":"Tucker","year":"1979","journal-title":"Remote Sens. Environ."},{"key":"ref_46","first-page":"1122","article-title":"Detecting major growth stages of paddy rice using MODIS data","volume":"13","author":"Sun","year":"2009","journal-title":"J. Remote Sens."},{"key":"ref_47","first-page":"732","article-title":"Comparison on three algorithms of reconstructing time-series MODIS EVI","volume":"17","author":"Wang","year":"2015","journal-title":"J. Geo-Inf. Sci."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Liu, M., Liu, X., Wu, L., Zou, X., Jiang, T., and Zhao, B. (2018). A modified spatiotemporal fusion algorithm using phenological information for predicting reflectance of paddy rice in southern China. Remote Sens., 10.","DOI":"10.3390\/rs10050772"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"833","DOI":"10.1016\/j.cageo.2004.05.006","article-title":"Timesat\u2014A program for analyzing time-series of satellite sensor data","volume":"30","author":"Eklundh","year":"2004","journal-title":"Comput. Geosci."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"7847","DOI":"10.1080\/01431161.2010.531783","article-title":"Classification of MODIS EVI time series for crop mapping in the state of Mato Grosso, Brazil","volume":"32","author":"Arvor","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_51","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_52","unstructured":"Nair, V., and Hinton, G.E. (2010, January 21\u201324). Rectified linear units improve restricted boltzmann machines. Proceedings of the 27th International Conference on International Conference on Machine Learning, Haifa, Israel."},{"key":"ref_53","unstructured":"Boureau, Y.L., Ponce, J., and Lecun, Y. (2010, January 21\u201324). A theoretical analysis of feature pooling in visual recognition. Proceedings of the 27th International Conference on International Conference on Machine Learning, Haifa, Israel."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1016\/j.engappai.2018.04.024","article-title":"Leukemia diagnosis in blood slides using transfer learning in CNNs and SVM for classification","volume":"72","author":"Vogado","year":"2018","journal-title":"Eng. Appl. Artif. Intell."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/7\/11\/418\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:26:55Z","timestamp":1760196415000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/7\/11\/418"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,10,30]]},"references-count":54,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2018,11]]}},"alternative-id":["ijgi7110418"],"URL":"https:\/\/doi.org\/10.3390\/ijgi7110418","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,10,30]]}}}