{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T11:56:19Z","timestamp":1773834979445,"version":"3.50.1"},"reference-count":49,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2017,9,2]],"date-time":"2017-09-02T00:00:00Z","timestamp":1504310400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Rural Development Administration, Republic of Korea"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>To improve the accuracy of classification with a small amount of training data, this paper presents a self-learning approach that defines class labels from sequential patterns using a series of past land-cover maps. By stacking past land-cover maps, unique sequence rule information from sequential change patterns of land-covers is first generated, and a rule-based class label image is then prepared for a given time. After the most informative pixels with high uncertainty are selected from the initial classification, rule-based class labels are assigned to the selected pixels. These newly labeled pixels are added to training data, which then undergo an iterative classification process until a stopping criterion is reached. Time-series MODIS NDVI data sets and cropland data layers (CDLs) from the past five years are used for the classification of various crop types in Kansas. From the experiment results, it is found that once the rule-based labels are derived from past CDLs, the labeled informative pixels could be properly defined without analyst intervention. Regardless of different combinations of past CDLs, adding these labeled informative pixels to training data increased classification accuracy and the maximum improvement of 8.34 percentage points in overall accuracy was achieved when using three CDLs, compared to the initial classification result using a small amount of training data. Using more than three consecutive CDLs showed slightly better classification accuracy than when using two CDLs (minimum and maximum increases were 1.56 and 2.82 percentage points, respectively). From a practical viewpoint, using three or four CDLs was the best choice for this study area. Based on these experiment results, the presented approach could be applied effectively to areas with insufficient training data but access to past land-cover maps. However, further consideration should be given to select the optimal number of past land-cover maps and reduce the impact of errors of rule-based labels.<\/jats:p>","DOI":"10.3390\/rs9090921","type":"journal-article","created":{"date-parts":[[2017,9,4]],"date-time":"2017-09-04T11:11:52Z","timestamp":1504523512000},"page":"921","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Self-Learning Based Land-Cover Classification Using Sequential Class Patterns from Past Land-Cover Maps"],"prefix":"10.3390","volume":"9","author":[{"given":"Yeseul","family":"Kim","sequence":"first","affiliation":[{"name":"Department of Geoinformatic Engineering, Inha University, Incheon 22212, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9778-3624","authenticated-orcid":false,"given":"No-Wook","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Geoinformatic Engineering, Inha University, Incheon 22212, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kyung-Do","family":"Lee","sequence":"additional","affiliation":[{"name":"National Institute of Agricultural Sciences, Rural Development Administration, Wanju 55365, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2017,9,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1096","DOI":"10.1016\/j.rse.2007.07.019","article-title":"Large-area crop mapping using time-series MODIS 250 m NDVI data: An assessment for the U.S. Central Great Plains","volume":"112","author":"Wardlow","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3212","DOI":"10.3390\/rs5073212","article-title":"Influence of multi-temporal remotely sensed and ancillary data on the accuracy of random forest classification of wetlands in Northern Minnesota","volume":"5","author":"Corcoran","year":"2013","journal-title":"Remote Sens."},{"key":"ref_3","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_4","doi-asserted-by":"crossref","unstructured":"Kong, F., Li, X., Wang, H., Xie, D., Li, X., and Bai, Y. (2016). Land cover classification based on fused data from GF-1 and MODIS NDVI time series. Remote Sens., 8.","DOI":"10.3390\/rs8090741"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Park, N.-W., Kyriakidis, P.C., and Hong, S. (2016). Spatial estimation of classification accuracy using indicator kriging with an image-derived ambiguity index. Remote Sens., 8.","DOI":"10.3390\/rs8040320"},{"key":"ref_6","first-page":"485","article-title":"Automatic verification of GIS data using high resolution multispectral data","volume":"32","author":"Walter","year":"1998","journal-title":"Int. Arch. Photogramm. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3858","DOI":"10.1109\/TGRS.2007.898446","article-title":"Fusion of support vector machines for classification of multisensor data","volume":"45","author":"Waske","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","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_9","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1016\/j.rse.2015.03.029","article-title":"Multi-sensor mapping of West African land cover using MODIS, ASAR and TanDEM-X\/TerraSAR-X data","volume":"164","author":"Gessner","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2440","DOI":"10.3390\/rs3112440","article-title":"An object-based classification of Mangroves using a hybrid decision tree-support vector machine approach","volume":"3","author":"Heumann","year":"2011","journal-title":"Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1080\/2150704X.2014.889863","article-title":"Random forest classification of crop type using multi-temporal TerraSAR-X dual-polarimetric data","volume":"5","author":"Sonobe","year":"2014","journal-title":"Remote Sens. Lett."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2912","DOI":"10.3390\/rs6042912","article-title":"Performance evaluation of machine learning algorithms for urban pattern recognition from multi-spectral satellite images","volume":"6","author":"Wieland","year":"2014","journal-title":"Remote Sens."},{"key":"ref_13","unstructured":"Zhu, X. (2005). Semi-Supervised Learning Literature Survey, Department of Computer Sciences, University of Wisconsin-Madison. Technical Report 1530."},{"key":"ref_14","unstructured":"Settles, B. (2010). Active Learning Literature Survey, Department of Computer Sciences, University of Wisconsin-Madison. Technical Report 1648."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2232","DOI":"10.1016\/j.rse.2011.04.022","article-title":"Using active learning to adapt remote sensing image classifiers","volume":"115","author":"Tuia","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2186","DOI":"10.1109\/TGRS.2013.2258468","article-title":"Bayesian active remote sensing image classification","volume":"52","author":"Ruiz","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"16024","DOI":"10.3390\/rs71215819","article-title":"Automatic labeling and selection of training samples for high-resolution remote sensing image classification over urban areas","volume":"7","author":"Huang","year":"2015","journal-title":"Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2384","DOI":"10.1109\/TGRS.2014.2359933","article-title":"Collaborative active and semisupervised learning for hyperspectral remote sensing image classification","volume":"53","author":"Wan","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"76","DOI":"10.11108\/kagis.2015.18.3.076","article-title":"Classification of crop cultivation areas using active learning and temporal contextual information","volume":"18","author":"Kim","year":"2015","journal-title":"J. Korean Assoc. Geogr. Inf. Stud."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2142","DOI":"10.1109\/TGRS.2008.2011983","article-title":"A novel context-sensitive semisupervised SVM classifier robust to mislabeled training samples","volume":"47","author":"Bruzzone","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"4801","DOI":"10.3390\/rs6064801","article-title":"Semi-supervised learning for ill-posed polarimetric SAR classification","volume":"6","author":"Uhlmann","year":"2014","journal-title":"Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Chapelle, O., Sch\u00f6lkopf, B., and Zien, A. (2006). Semi-Supervised Learning, The MIT Press.","DOI":"10.7551\/mitpress\/9780262033589.001.0001"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.knosys.2013.01.032","article-title":"Combining active learning and semi-supervised learning to construct SVM classifier","volume":"44","author":"Leng","year":"2013","journal-title":"Knowl.-Based Syst."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3751","DOI":"10.1109\/TGRS.2012.2185504","article-title":"Semisupervised classification of remote sensing images with active queries","volume":"50","author":"Tuia","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"4032","DOI":"10.1109\/TGRS.2012.2228275","article-title":"Semisupervised self-learning for hyperspectral image classification","volume":"51","author":"Li","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Blum, A., and Mitchell, T. (1998, January 24\u201326). Combining Labeled Data and Unlabeled Data with Co-training. Proceedings of the Eleventh Annual Conference on Computational Learning Theory, Madison, WI, USA.","DOI":"10.1145\/279943.279962"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1080\/10106049.2011.562309","article-title":"Monitoring US agriculture: The US department of agriculture, national statistics service, cropland data layer program","volume":"26","author":"Boryan","year":"2011","journal-title":"Geocarto Int."},{"key":"ref_28","unstructured":"(2017, March 01). CropScape. Available online: https:\/\/nassgeodata.gmu.edu\/CropScape."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"805","DOI":"10.1080\/01431160902897858","article-title":"A comparison of MODIS 250-m EVI and NDVI data for crop mapping: A case study for southwest Kansas","volume":"31","author":"Wardlow","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"8763","DOI":"10.1080\/01431161.2010.550647","article-title":"Temporal segmentation of MODIS time series for improving crop classification in Central Asian irrigation systems","volume":"32","author":"Conrad","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"493","DOI":"10.7780\/kjrs.2014.30.4.7","article-title":"Early production of large-area crop classification map using time-series vegetation index and past crop cultivation patterns","volume":"30","author":"Kim","year":"2014","journal-title":"Korean J. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1016\/j.rse.2004.03.014","article-title":"A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter","volume":"91","author":"Chen","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1778","DOI":"10.1109\/TGRS.2004.831865","article-title":"Classification of hyperspectral remote sensing images with support vector machines","volume":"42","author":"Melgani","year":"2004","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2227","DOI":"10.1080\/01431160701395203","article-title":"Crop classification by support vector machine with intelligently selected training data for an operational application","volume":"29","author":"Mathur","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_35","unstructured":"Vapnik, V. (1998). Statistical Learning Theory, Wiley."},{"key":"ref_36","first-page":"975","article-title":"Probability estimates for multi-class classification by pairwise coupling","volume":"5","author":"Wu","year":"2004","journal-title":"J. Mach. Learn. Res."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2218","DOI":"10.1109\/TGRS.2008.2010404","article-title":"Active learning methods for remote sensing image classification","volume":"47","author":"Tuia","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"606","DOI":"10.1109\/JSTSP.2011.2139193","article-title":"A survey of active learning algorithms for supervised remote sensing image classification","volume":"5","author":"Tuia","year":"2011","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"3947","DOI":"10.1109\/TGRS.2011.2128330","article-title":"Hyperspectral image segmentation using new Bayesian approach with active learning","volume":"49","author":"Li","year":"2011","journal-title":"IEEE Trans. Geosci. Remote."},{"key":"ref_40","first-page":"589","article-title":"Active learning to recognize multiple types of plankton","volume":"6","author":"Luo","year":"2005","journal-title":"J. Mach. Learn. Res."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.apsoil.2007.03.009","article-title":"Microbial communities and enzyme activities in soils under alternative crop rotations compared to wheat-fallow for the Central Great Plains","volume":"37","author":"Mikha","year":"2007","journal-title":"Appl. Soil Ecol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.agee.2009.11.008","article-title":"Long-term impacts of high-input annual cropping and unfertilized perennial grass production on soil properties and belowground food webs in Kansas, USA","volume":"137","author":"Culman","year":"2010","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1016\/j.rse.2006.11.021","article-title":"Analysis of time-series MODIS 250 m vegetation index data for crop classification in the U.S. Central Great Plains","volume":"108","author":"Wardlow","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"627","DOI":"10.14358\/PERS.70.5.627","article-title":"Thematic map comparison: Evaluating the statistical significance of differences in classification accuracy","volume":"70","author":"Foody","year":"2008","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_45","unstructured":"Lillesand, T.M., Kiefer, R.W., and Chipman, J.W. (2008). Remote Sensing and Image Interpretation, Wiley. [6th ed.]."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1016\/j.compag.2015.02.015","article-title":"Assessment of a Markov logic model of crop rotation for early crop mapping","volume":"113","author":"Osman","year":"2015","journal-title":"Comput. Electron. Agric."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Pelletier, C., Valero, S., Inglada, J., Champion, N., Sicre, C.M., and Dedieu, G. (2017). Effect of training class label noise on classification performances for land cover mapping with satellite image time series. Remote Sens., 9.","DOI":"10.3390\/rs9020173"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"827586","DOI":"10.1155\/2014\/827586","article-title":"An active learning approach with uncertainty, representativeness, and diversity","volume":"2014","author":"He","year":"2014","journal-title":"Sci. World J."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1109\/LGRS.2013.2246539","article-title":"An effective strategy to reduce the labeling cost in the definition of training sets by active learning","volume":"11","author":"Demir","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/9\/921\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:43:59Z","timestamp":1760208239000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/9\/921"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,9,2]]},"references-count":49,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2017,9]]}},"alternative-id":["rs9090921"],"URL":"https:\/\/doi.org\/10.3390\/rs9090921","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,9,2]]}}}