{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,22]],"date-time":"2025-12-22T18:33:03Z","timestamp":1766428383082,"version":"build-2065373602"},"reference-count":36,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2017,9,22]],"date-time":"2017-09-22T00:00:00Z","timestamp":1506038400000},"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>Time series land cover maps play a key role in monitoring the dynamic change of land use. To obtain classification maps with better spatial-temporal consistency and classification accuracy, this study used an algorithm that incorporated information from spatial and temporal neighboring observations in a hidden Markov model (HMM) to improve the time series land cover maps initially produced by a support vector machine (SVM). To investigate the effects of different initial distributions and transition probability matrices on the classification of the HMM, we designed different experimental schemes with different input elements to verify this algorithm with Landsat and HJ satellite images. In addition, we introduced spatial weights into the HMM to make effective use of spatial information. The experimental results showed that the HMM considered that spatial weights could eliminate the vast majority of illogical land cover transition that may occur in previous pixel-wise classification, and that this model had obvious advantages in spatial-temporal consistency and classification accuracy over some existing classification models.<\/jats:p>","DOI":"10.3390\/ijgi6100292","type":"journal-article","created":{"date-parts":[[2017,9,22]],"date-time":"2017-09-22T11:03:15Z","timestamp":1506078195000},"page":"292","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Using a Hidden Markov Model for Improving the Spatial-Temporal Consistency of Time Series Land Cover Classification"],"prefix":"10.3390","volume":"6","author":[{"given":"Wenbing","family":"Gong","sequence":"first","affiliation":[{"name":"School of Remote Sensing Information Engineering, Wuhan University, Wuhan 430079, China"},{"name":"Remote-Sensing Phenomics and Hybrid Rice Precision Breeding Lab, Wuhan University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shenghui","family":"Fang","sequence":"additional","affiliation":[{"name":"School of Remote Sensing Information Engineering, Wuhan University, Wuhan 430079, China"},{"name":"Remote-Sensing Phenomics and Hybrid Rice Precision Breeding Lab, Wuhan University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guang","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Remote Sensing Information Engineering, Wuhan University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mengyu","family":"Ge","sequence":"additional","affiliation":[{"name":"School of Remote Sensing Information Engineering, Wuhan University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2017,9,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"7959","DOI":"10.3390\/rs70607959","article-title":"Mapping priorities to focus cropland mapping activities: Fitness assessment of existing global, regional and national cropland maps","volume":"7","author":"Waldner","year":"2015","journal-title":"Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1080\/10106049.2011.562309","article-title":"Monitoring US agriculture: The US Department of Agriculture, National Agricultural Statistics Service, Cropland Data Layer Program","volume":"26","author":"Boryan","year":"2011","journal-title":"Geocarto Int."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.apgeog.2008.02.001","article-title":"Land-cover and land-use change in a mediterranean landscape: A spatial analysis of driving forces integrating biophysical and human factors","volume":"28","author":"Serra","year":"2008","journal-title":"Appl. Geogr."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1016\/j.apgeog.2008.12.005","article-title":"Land use and land cover change in greater dhaka, bangladesh: Using remote sensing to promote sustainable urbanization","volume":"29","author":"Dewan","year":"2009","journal-title":"Appl. Geogr."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.rse.2004.09.005","article-title":"A comparative analysis of the global land cover 2000 and modis land cover data sets","volume":"94","author":"Giri","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"534","DOI":"10.1016\/j.rse.2006.01.020","article-title":"Exploiting synergies of global land cover products for carbon cycle modeling","volume":"101","author":"Jung","year":"2006","journal-title":"Remote Sen. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3009","DOI":"10.1080\/01431160500057848","article-title":"Improving land cover change estimates by accounting for classification errors","volume":"26","year":"2005","journal-title":"Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"478","DOI":"10.1016\/j.rse.2014.11.024","article-title":"Improved time series land cover classification by missing-observation-adaptive nonlinear dimensionality reduction","volume":"158","author":"Yan","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Yang, G., Fang, S., Dian, Y., and Bi, C. (2016). Improving Seasonal Land Cover Maps of Poyang Lake Area in China by Taking into Account Logical Transitions. Int. J. Geo-Inf., 5.","DOI":"10.3390\/ijgi5090165"},{"key":"ref_10","first-page":"277","article-title":"Time-series analysis of multi-resolution optical imagery for quantifying forest cover loss in Sumatra and Kalimantan, Indonesia","volume":"13","author":"Broich","year":"2011","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.isprsjprs.2014.03.008","article-title":"Historical forest biomass dynamics modelled with Landsat spectral trajectories","volume":"93","author":"White","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1016\/j.rse.2013.03.022","article-title":"Consistent classification of image time series with automatic adaptive signature generalization","volume":"134","author":"Gray","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1016\/j.rse.2005.12.012","article-title":"Optical remotelyatial-temporal approach to monitoring forest disease spread using multi-temporal high spatial resolution imagery","volume":"101","author":"Liu","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/j.rse.2014.03.012","article-title":"Enhancing MODIS land cover product with a spatial-temporal modeling algorithm","volume":"147","author":"Cai","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1815","DOI":"10.1109\/TGRS.2002.802498","article-title":"An image change detection algorithm based on Markov random field models","volume":"40","author":"Kasetkasem","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.isprsjprs.2014.03.007","article-title":"Mapping global land cover in 2001 and 2010 with spatial-temporal consistency at 250 m resolution","volume":"103","author":"Wang","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.isprsjprs.2015.04.009","article-title":"A spatial-temporal contextual Markovian kernel method for multi-temporal land cover mapping","volume":"107","author":"Wehmann","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1109\/5.18626","article-title":"A tutorial on hidden Markov models and selected applications in speech recognition","volume":"77","author":"Rabiner","year":"1989","journal-title":"Proc. IEEE"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Aurdal, L., Huseby, R.B., Eikvil, L., Solberg, R., Vikhamar, D., and Solberg, A. (2005). Use of hidden Markov models and phenology for multitemporal satellite image classification: Applications to mountain vegetation classification. Int. Workshop Anal. Multi-Temporal Remote Sens. Images, 220\u2013224.","DOI":"10.1109\/AMTRSI.2005.1469877"},{"key":"ref_20","unstructured":"Vapnik, V. (1998). Statistical Learning Theory, Springer Press."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"851","DOI":"10.1162\/neco.1994.6.5.851","article-title":"Measuring the VC-dimension of a learning machine","volume":"6","author":"Vapnik","year":"1994","journal-title":"Neural Comput."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Salberg, A.B., and Trier, D. (2011). Temporal analysis of forest cover using hidden Markov models. IEEE Geosci. Remote Sens. Symp., 2322\u20132325.","DOI":"10.1109\/IGARSS.2011.6049674"},{"key":"ref_23","unstructured":"Liu, X.L. (2013). An Ecological Environment Quality Evaluation of Remote Sensing of Poyang Lake Ecological Economic Region in 2000\u20132010, Jiangxi Normal University."},{"key":"ref_24","unstructured":"Kohavi, R. (1995, January 20\u201325). A study of cross-validation and bootstrap for accuracy estimation and model selection. Proceedings of the 14th international joint conference on Artificial intelligence, Montreal, QC, Canada."},{"key":"ref_25","first-page":"4525","article-title":"The dynamic change pattern of land use and cover in recent 20 years in Guanzhong area","volume":"44","author":"Hao","year":"2011","journal-title":"Sci. Agric. Sin."},{"key":"ref_26","first-page":"312","article-title":"The temporal-spatial characteristics of dynamic land cover change in 1999\u20132009 in China","volume":"27","author":"Li","year":"2011","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_27","first-page":"270","article-title":"The regional differential regularity of dynamic land cover change range in China","volume":"24","author":"Li","year":"2004","journal-title":"Sci. Geogr. Sin."},{"key":"ref_28","unstructured":"Tan, Q. (2002). A Research on Remote Sensing Change Detection of Wetland Ecosystem in Poyang Lake, Institute of Remote Sensing Application Chinese Academy of Sciences."},{"key":"ref_29","first-page":"8","article-title":"Ecological Effect Analysis of Poyang Lake Wetland Based on LUCC","volume":"38","author":"Zhu","year":"2017","journal-title":"J. Water Ecol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"703","DOI":"10.1109\/TGRS.2015.2463689","article-title":"Improving the Consistency of Multitemporal Land Cover Maps Using a Hidden Markov Model","volume":"54","author":"Abercrombie","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1657","DOI":"10.1109\/TIP.2010.2044957","article-title":"A completed modeling of local binary pattern operator for texture classification","volume":"19","author":"Guo","year":"2010","journal-title":"IEEE Trans. Image Process."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Ahonen, T., Matas, J., He, C., and Pietik\u00e4inen, M. (2009). Rotation Invariant Image Description with Local Binary Pattern Histogram Fourier Features. Image Anal., 61\u201370.","DOI":"10.1007\/978-3-642-02230-2_7"},{"key":"ref_33","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_34","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.isprsjprs.2013.01.010","article-title":"Spatiotemporal dynamic of surface water bodies using Landsat time-series data from 1999 to 2011","volume":"79","author":"Tulbure","year":"2013","journal-title":"ISPRS. J. Photogramm. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2495","DOI":"10.1016\/j.rse.2007.11.012","article-title":"A method for integrating MODIS and Landsat data for systematic monitoring of forest cover and change in the Congo Basin","volume":"112","author":"Hansen","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2478","DOI":"10.1109\/TGRS.2003.817269","article-title":"A Markov random field approach to spatio-temporal contextual image classification","volume":"41","author":"Melgani","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/6\/10\/292\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:45:36Z","timestamp":1760208336000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/6\/10\/292"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,9,22]]},"references-count":36,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2017,10]]}},"alternative-id":["ijgi6100292"],"URL":"https:\/\/doi.org\/10.3390\/ijgi6100292","relation":{},"ISSN":["2220-9964"],"issn-type":[{"type":"electronic","value":"2220-9964"}],"subject":[],"published":{"date-parts":[[2017,9,22]]}}}