{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T12:30:17Z","timestamp":1781872217336,"version":"3.54.5"},"reference-count":110,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2020,4,2]],"date-time":"2020-04-02T00:00:00Z","timestamp":1585785600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology, Taiwan","doi-asserted-by":"publisher","award":["MOST 108-2111-M-008 -036 -MY2 and MOST 108-2923-M-008 -002 -MY3"],"award-info":[{"award-number":["MOST 108-2111-M-008 -036 -MY2 and MOST 108-2923-M-008 -002 -MY3"]}],"id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Rapid and uncontrolled population growth along with economic and industrial development, especially in developing countries during the late twentieth and early twenty-first centuries, have increased the rate of land-use\/land-cover (LULC) change many times. Since quantitative assessment of changes in LULC is one of the most efficient means to understand and manage the land transformation, there is a need to examine the accuracy of different algorithms for LULC mapping in order to identify the best classifier for further applications of earth observations. In this article, six machine-learning algorithms, namely random forest (RF), support vector machine (SVM), artificial neural network (ANN), fuzzy adaptive resonance theory-supervised predictive mapping (Fuzzy ARTMAP), spectral angle mapper (SAM) and Mahalanobis distance (MD) were examined. Accuracy assessment was performed by using Kappa coefficient, receiver operational curve (RoC), index-based validation and root mean square error (RMSE). Results of Kappa coefficient show that all the classifiers have a similar accuracy level with minor variation, but the RF algorithm has the highest accuracy of 0.89 and the MD algorithm (parametric classifier) has the least accuracy of 0.82. In addition, the index-based LULC and visual cross-validation show that the RF algorithm (correlations between RF and normalised differentiation water index, normalised differentiation vegetation index and normalised differentiation built-up index are 0.96, 0.99 and 1, respectively, at 0.05 level of significance) has the highest accuracy level in comparison to the other classifiers adopted. Findings from the literature also proved that ANN and RF algorithms are the best LULC classifiers, although a non-parametric classifier like SAM (Kappa coefficient 0.84; area under curve (AUC) 0.85) has a better and consistent accuracy level than the other machine-learning algorithms. Finally, this review concludes that the RF algorithm is the best machine-learning LULC classifier, among the six examined algorithms although it is necessary to further test the RF algorithm in different morphoclimatic conditions in the future.<\/jats:p>","DOI":"10.3390\/rs12071135","type":"journal-article","created":{"date-parts":[[2020,4,2]],"date-time":"2020-04-02T11:57:14Z","timestamp":1585828634000},"page":"1135","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1045,"title":["Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations\u2014A Review"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6680-9791","authenticated-orcid":false,"given":"Swapan","family":"Talukdar","sequence":"first","affiliation":[{"name":"Department of Geography, University of Gour Banga, NH12, Mokdumpur, Malda-732103, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pankaj","family":"Singha","sequence":"additional","affiliation":[{"name":"Department of Geography, University of Gour Banga, NH12, Mokdumpur, Malda-732103, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6437-2642","authenticated-orcid":false,"given":"Susanta","family":"Mahato","sequence":"additional","affiliation":[{"name":"Department of Geography, University of Gour Banga, NH12, Mokdumpur, Malda-732103, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5868-1062","authenticated-orcid":false,"family":"Shahfahad","sequence":"additional","affiliation":[{"name":"Department of Geography, Faculty of Natural Sciences, Jamia Millia Islamia, MMAJ Marg, Jamia Nagar, New Delhi-110025, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Swades","family":"Pal","sequence":"additional","affiliation":[{"name":"Department of Geography, University of Gour Banga, NH12, Mokdumpur, Malda-732103, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8100-5529","authenticated-orcid":false,"given":"Yuei-An","family":"Liou","sequence":"additional","affiliation":[{"name":"Center for Space and Remote Sensing Research, National Central University, 300, Jhongda Rd., Jhongli District, Taoyuan City 32001, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Atiqur","family":"Rahman","sequence":"additional","affiliation":[{"name":"Department of Geography, Faculty of Natural Sciences, Jamia Millia Islamia, MMAJ Marg, Jamia Nagar, New Delhi-110025, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,4,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1080\/19475683.2014.992369","article-title":"Change analysis of land use\/land cover and modelling urban growth in Greater Doha, Qatar","volume":"21","author":"Hashem","year":"2015","journal-title":"Ann. Gis"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"689","DOI":"10.1007\/s12524-011-0165-4","article-title":"Assessment of land use\/land cover change in the North-West District of Delhi using remote sensing and GIS techniques","volume":"40","author":"Rahman","year":"2012","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.ecolind.2017.04.055","article-title":"Assessing spatiotemporal eco-environmental vulnerability by Landsat data","volume":"80","author":"Liou","year":"2017","journal-title":"Ecol. Indic."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"862","DOI":"10.1016\/j.mex.2019.03.023","article-title":"Mapping global eco-environment vulnerability due to human and nature disturbances","volume":"6","author":"Nguyen","year":"2019","journal-title":"MethodsX"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"995","DOI":"10.1016\/j.scitotenv.2019.01.407","article-title":"Global mapping of eco-environmental vulnerability from human and nature disturbances","volume":"664","author":"Nguyen","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Talukdar, S., and Pal, S. (2018). Wetland habitat vulnerability of lower Punarbhaba river basin of the uplifted Barind region of Indo-Bangladesh. Geocarto Int., 1\u201330.","DOI":"10.1080\/10106049.2018.1533594"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.ecolind.2016.03.026","article-title":"Zoning eco-environmental vulnerability for environmental management and protection","volume":"69","author":"Nguyen","year":"2016","journal-title":"Ecol. Indic."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"903709","DOI":"10.1155\/2014\/903709","article-title":"Changes in glaciers and glacial lakes and the identification of dangerous glacial lakes in the Pumqu River Basin, Xizang (Tibet)","volume":"2014","author":"Che","year":"2014","journal-title":"Adv. Meteorol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"5077","DOI":"10.3390\/rs70505077","article-title":"Object-based flood mapping and affected rice field estimation with Landsat 8 OLI and MODIS data","volume":"7","author":"Dao","year":"2015","journal-title":"Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3355","DOI":"10.1080\/01431161003727655","article-title":"Use of high-resolution FORMOSAT-2 satellite images for post-earthquake disaster assessment: A study following the 12 May 2008 Wenchuan Earthquake","volume":"31","author":"Liou","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_11","first-page":"618","article-title":"Assessment of disaster losses in rice paddy field and yield after Tsunami induced by the 2011 great east Japan earthquake","volume":"20","author":"Liou","year":"2012","journal-title":"J. Mar. Sci. Technol."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Ge, T., Tian, W., and Liou, Y.A. (2019). Debris Flow Susceptibility Mapping Using Machine-Learning Techniques in Shigatse Area, China. Remote Sens., 11.","DOI":"10.3390\/rs11232801"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.ecoleng.2019.05.014","article-title":"Effects of damming on the hydrological regime of Punarbhaba river basin wetlands","volume":"135","author":"Talukdar","year":"2019","journal-title":"Ecol. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"106121","DOI":"10.1016\/j.ecolind.2020.106121","article-title":"Dynamics of ecosystem services (ESs) in response to land use land cover (LU\/LC) changes in the lower Gangetic plain of India","volume":"112","author":"Talukdar","year":"2020","journal-title":"Ecol. Indic."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1097","DOI":"10.1016\/j.scitotenv.2018.08.141","article-title":"Land-use change from arable lands to orchards reduced soil erosion and increased nutrient loss in a small catchment","volume":"648","author":"Chen","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Braun, A., and Hochschild, V. (2017). A SAR-Based Index for Landscape Changes in African Savannas. Remote Sens., 9.","DOI":"10.3390\/rs9040359"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40645-019-0311-0","article-title":"Soil salinity assessment by using near-infrared channel and Vegetation Soil Salinity Index derived from Landsat 8 OLI data: A case study in the Tra Vinh Province, Mekong Delta, Vietnam","volume":"7","author":"Nguyen","year":"2020","journal-title":"Prog. Earth Planet. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"626","DOI":"10.1080\/22797254.2017.1387505","article-title":"Land use\/land cover change detection combining automatic processing and visual interpretation","volume":"50","author":"Mas","year":"2017","journal-title":"Eur. J. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"6188","DOI":"10.3390\/s8106188","article-title":"Analyzing Land Use\/Land Cover Changes Using Remote Sensing and GIS in Rize, North-East Turkey","volume":"8","author":"Reis","year":"2008","journal-title":"Sensors"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1007\/s10661-019-7645-3","article-title":"Changing pattern of urban landscape and its effect on land surface temperature in and around Delhi","volume":"191","author":"Dutta","year":"2019","journal-title":"Environ. Monit. Assess."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Hoan, N.T., Liou, Y.A., Nguyen, K.A., Sharma, R.C., Tran, D.P., Liou, C.L., and Cham, D.D. (2018). Assessing the Effects of Land-Use Types in Surface Urban Heat Islands for Developing Comfortable Living in Hanoi City. Remote Sens., 10.","DOI":"10.3390\/rs10121965"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1109\/JSTARS.2010.2084072","article-title":"Monitoring Urban Sprawl Using Remote Sensing and GIS Techniques of a Fast Growing Urban Centre, India","volume":"4","author":"Rahman","year":"2011","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"368","DOI":"10.1007\/s42452-019-0372-0","article-title":"Assessment of public open spaces (POS) and landscape quality based on per capita POS index in Delhi, India","volume":"1","author":"Kumari","year":"2019","journal-title":"SN Appl. Sci."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1007\/s10668-018-0205-0","article-title":"Assessing the role of hydrological modifications on land use\/land cover dynamics in Punarbhaba river basin of Indo-Bangladesh","volume":"22","author":"Pal","year":"2018","journal-title":"Environ. Dev. Sustain."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2167","DOI":"10.1002\/esp.3969","article-title":"Influence of anthropogenic land-use change on hillslope erosion in the Waipaoa River Basin, New Zealand","volume":"41","author":"Roering","year":"2016","journal-title":"Earth Surf. Process. Landf."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"120311","DOI":"10.1016\/j.jclepro.2020.120311","article-title":"Groundwater potential zones for sustainable management plans in a river basin of India and Bangladesh","volume":"257","author":"Pal","year":"2020","journal-title":"J. Clean. Prod."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1007\/s11053-018-9404-5","article-title":"Groundwater potential mapping in a rural river basin by union (OR) and intersection (AND) of four multi-criteria decision-making models","volume":"28","author":"Mahato","year":"2019","journal-title":"Nat. Resour. Res."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Langat, P.K., Kumar, L., Koech, R., and Ghosh, M.K. (2019). Monitoring of land use\/land-cover dynamics using remote sensing: A case of Tana River Basin, Kenya. Geocarto Int.","DOI":"10.1080\/10106049.2019.1655798"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1007\/s10040-004-0409-2","article-title":"The future of satellite remote sensing in hydrogeology","volume":"13","author":"Hoffmann","year":"2005","journal-title":"Hydrogeol. J."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Qian, S.-E. (2016). FORMOSAT-2 Quick Imaging. Optical Payloads for Space Missions, Wiley.","DOI":"10.1002\/9781118945179"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"4405","DOI":"10.1080\/01431160801905497","article-title":"Expert system classification of urban land use\/cover for Delhi, India","volume":"29","author":"Wentz","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"9600","DOI":"10.3390\/rs6109600","article-title":"Remote Sensing for Landslide Investigations: An Overview of Recent Achievements and Perspectives","volume":"6","author":"Scaioni","year":"2014","journal-title":"Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1519","DOI":"10.1080\/01431160903475381","article-title":"Land use and land cover change detection using satellite remote sensing techniques in the mountainous Three Gorges Area, China","volume":"31","author":"Chen","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1215","DOI":"10.1016\/j.future.2003.11.011","article-title":"Assessment of the effectiveness of support vector machines for hyperspectral data","volume":"20","author":"Pal","year":"2004","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_35","first-page":"167","article-title":"Comparison of two dimensional multitemporal Sentinel-2 data with three-dimensional remote sensing data sources for forest inventory parameter estimation over a boreal forest","volume":"76","author":"Wittke","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Viana, C.M., Gir\u00e3o, I., and Rocha, J. (2019). Long-Term Satellite Image Time-Series for Land Use\/Land Cover Change Detection Using Refined Open Source Data in a Rural Region. Remote Sens., 11.","DOI":"10.3390\/rs11091104"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1007\/s10661-019-7356-9","article-title":"Estimating long-term LULC changes in an agriculture-dominated basin using CORONA (1970) and LISS IV (2013\u201314) satellite images: A case study of Ramganga River, India","volume":"191","author":"Gurjar","year":"2019","journal-title":"Environ. Monitor. Assess."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/j.rse.2018.03.023","article-title":"Land cover and land use change analysis using multi-spatial resolution data and object-based image analysis","volume":"210","author":"Toure","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1479","DOI":"10.1007\/s11442-015-1247-y","article-title":"Land use\/land cover classification and its change detection using multi-temporal MODIS NDVI data","volume":"25","author":"Usman","year":"2015","journal-title":"J. Geogr. Sci."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.rse.2005.04.024","article-title":"Assessment of ASTER Land Cover and MODIS NDVI Data at Multiple Scales for Ecological Characterization of an Arid Urban Center","volume":"99","author":"Stefanov","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2784","DOI":"10.1080\/01431161.2018.1433343","article-title":"Implementation of machine-learning classification in remote sensing: An applied review","volume":"39","author":"Maxwell","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"3440","DOI":"10.1080\/01431161.2014.903435","article-title":"Land-use\/cover classification in a heterogeneous coastal landscape using Rapid Eye imagery: Evaluating the performance of random forest and support vector machines classifiers","volume":"35","author":"Adam","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"5973","DOI":"10.1080\/01431161.2019.1584929","article-title":"Comparison of machine learning algorithms for classification of LiDAR points for characterization of canola canopy structure","volume":"40","author":"Wu","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"5770","DOI":"10.1016\/j.asoc.2011.02.030","article-title":"Supervised and unsupervised landuse map generation from remotely sensed images using ant based systems","volume":"11","author":"Halder","year":"2011","journal-title":"Appl. Soft Comput."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.isprsjprs.2019.04.015","article-title":"Deep learning in remote sensing applications: A meta-analysis and review","volume":"152","author":"Ma","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1248","DOI":"10.1080\/01431161.2018.1524179","article-title":"Guidance on and comparison of machine learning classifiers for Landsat-based land cover and land use mapping","volume":"40","author":"Shih","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1109\/JSTSP.2011.2142490","article-title":"Introduction to the issue on advances in remote sensing image processing","volume":"5","author":"Benediktsson","year":"2011","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.rse.2018.11.014","article-title":"Joint Deep Learning for land cover and land use classification","volume":"221","author":"Zhang","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1016\/j.isprsjprs.2018.07.017","article-title":"A 30-m Landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud computing platform","volume":"144","author":"Teluguntla","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1080\/01431160412331269698","article-title":"Random forest classifier for remote sensing classification","volume":"26","author":"Pal","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1080\/02693799308901949","article-title":"Artificial neural networks for land-cover classification and mapping","volume":"7","author":"Civco","year":"1993","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Camargo, F.F., Sano, E.E., Almeida, C.M., Mura, J.C., and Almeida, T. (2019). A comparative assessment of machine-learning techniques for land use and land cover classification of the Brazilian tropical savanna using ALOS-2\/PALSAR-2 polarimetric images. Remote Sens., 11.","DOI":"10.3390\/rs11131600"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Li, X., Chen, W., Cheng, X., and Wang, L. (2016). A comparison of machine learning algorithms for mapping of complex surface-mined and agricultural landscapes using ZiYuan-3 stereo satellite imagery. Remote Sens., 8.","DOI":"10.3390\/rs8060514"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"2272","DOI":"10.1016\/j.rse.2007.10.004","article-title":"Mapping land-cover modifications over large areas: A comparison of machine learning algorithms","volume":"112","author":"Rogan","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1448","DOI":"10.1007\/s42452-019-1527-8","article-title":"Evaluation and comparison of eight machine learning models in land use\/land cover mapping using Landsat 8 OLI: A case study of the northern region of Iran","volume":"1","author":"Jamali","year":"2019","journal-title":"SN Appl. Sci."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Carranza-Garc\u00eda, M., Garc\u00eda-Guti\u00e9rrez, J., and Riquelme, J.C. (2019). A framework for evaluating land use and land cover classification using convolutional neural networks. Remote Sens., 11.","DOI":"10.3390\/rs11030274"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/j.isprsjprs.2017.06.001","article-title":"A review of supervised object-based land-cover image classification","volume":"130","author":"Ma","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_58","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_59","doi-asserted-by":"crossref","first-page":"4823","DOI":"10.1080\/01431160801950162","article-title":"PCA-based land-use change detection and analysis using multitemporal and multisensor satellite data","volume":"29","author":"Deng","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Yang, C., Wu, G., Ding, K., Shi, T., Li, Q., and Wang, J. (2017). Improving land use\/land cover classification by integrating pixel unmixing and decision tree methods. Remote Sens., 9.","DOI":"10.3390\/rs9121222"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"330","DOI":"10.3390\/rs1030330","article-title":"Improving the accuracy of land use and land cover classification of Landsat data using post-classification enhancement","volume":"1","author":"Manandhar","year":"2009","journal-title":"Remote Sens."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.rse.2003.11.016","article-title":"Accuracy assessment using sub-pixel fractional error matrices of global land cover products derived from satellite data","volume":"90","author":"Latifovic","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_63","first-page":"125","article-title":"Detection of land use and land cover change and land surface temperature in English Bazar urban centre","volume":"20","author":"Pal","year":"2017","journal-title":"Egypt. J. Remote Sens. Space Sci."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1080\/09709274.2010.11906290","article-title":"Flood and Erosion Induced Population Displacements: A Socio-economic Case Study in the Gangetic Riverine Tract at Malda District, West Bengal, India","volume":"30","author":"Iqbal","year":"2010","journal-title":"J. Human Ecol."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"699","DOI":"10.1080\/014311697218700","article-title":"Introduction neural networks in remote sensing","volume":"18","author":"Atkinson","year":"1997","journal-title":"Int. J. Remote Sens."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Schuman, C.D., and Birdwell, J.D. (2013). Dynamic artificial neural networks with affective systems. PLoS ONE, 8.","DOI":"10.1371\/journal.pone.0080455"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"2718","DOI":"10.1109\/36.803419","article-title":"A neural network approach to radiometric sensing of land surface parameters","volume":"37","author":"Liou","year":"1999","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"1662","DOI":"10.1109\/36.942544","article-title":"Retrieving soil moisture from simulated brightness temperatures by a neural network","volume":"39","author":"Liou","year":"2001","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"823","DOI":"10.1080\/01431160600746456","article-title":"A survey of image classification methods and techniques for improving classification performance","volume":"28","author":"Lu","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1185","DOI":"10.1080\/01431160701294661","article-title":"Multispectral landuse classification using neural networks and support vector machines: One or the other, or both?","volume":"29","author":"Dixon","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"5958","DOI":"10.1016\/j.eswa.2010.11.027","article-title":"Multiple regression, ANN (RBF, MLP) and ANFIS models for prediction of swell potential of clayey soils","volume":"38","author":"Yilmaz","year":"2011","journal-title":"Expert Syst. Appl."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1162\/neco.1989.1.2.281","article-title":"Fast learning in networks of locally-tuned processing units","volume":"1","author":"Moody","year":"1989","journal-title":"Neural Comput."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"606","DOI":"10.1080\/13658816.2014.993989","article-title":"Performance analysis of radial basis function networks and multi-layer perceptron networks in modelling urban change: A case study","volume":"29","author":"Hagenauer","year":"2015","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"156","DOI":"10.4236\/jsea.2012.53023","article-title":"Evaluation of stiffened end-plate moment connection through optimized artificial neural network","volume":"5","author":"Ghassemieh","year":"2012","journal-title":"J. Softw. Eng. Appl."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"725","DOI":"10.1080\/01431160110040323","article-title":"An assessment of support vector machines for land cover classification","volume":"23","author":"Huang","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1007\/s41207-017-0036-7","article-title":"Semiautomatic approach for land cover classification: A remote sensing study for arid climate in Southeastern Tunisia","volume":"2","author":"Bouaziz","year":"2017","journal-title":"Euro Mediterr. J. Environ. Integr."},{"key":"ref_77","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_78","doi-asserted-by":"crossref","unstructured":"Awad, M., and Khanna, R. (2015). Support vector machines for classification. Efficient Learning Machines, Apress.","DOI":"10.1007\/978-1-4302-5990-9"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1109\/LGRS.2008.915597","article-title":"Multiclass and binary SVM classification: Implications for training and classification users","volume":"5","author":"Mathur","year":"2008","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"767","DOI":"10.1080\/014311698215991","article-title":"Fuzzy ARTMAP supervised classification of multi-spectral remotely-sensed images","volume":"19","author":"Mannan","year":"1998","journal-title":"Int. J. Remote Sens."},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Gopal, S. (2006). Fuzzy ARTMAP\u2014A neural classifier for multispectral image classification. Spatial Analysis and GeoComputation, Springer.","DOI":"10.1007\/3-540-35730-0_11"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Abdullah, A.Y.M., Masrur, A., Adnan, M.S.G., Baky, M., Al, A., Hassan, Q.K., and Dewan, A. (2019). Spatio-temporal patterns of land use\/land cover change in the heterogeneous coastal region of Bangladesh between 1990 and 2017. Remote Sens., 11.","DOI":"10.3390\/rs11070790"},{"key":"ref_84","first-page":"18","article-title":"Classification and Regression by randomForest","volume":"2","author":"Liaw","year":"2002","journal-title":"R News"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"12539","DOI":"10.3390\/rs70912539","article-title":"Flood mapping based on multiple endmember spectral mixture analysis and random forest classifier\u2014The case of Yuyao, China","volume":"7","author":"Feng","year":"2015","journal-title":"Remote Sens."},{"key":"ref_86","first-page":"240","article-title":"Class-Specific Mahalanobis Distance Metric Learning for Biological Image Classification","volume":"Volume 7325","author":"Campilho","year":"2012","journal-title":"Image Analysis and Recognition\u20149th International Conference, ICIAR 2012, Aveiro, Portugal, 25\u201327 June 2012"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"1967","DOI":"10.3390\/s100301967","article-title":"A Comparison of Spectral Angle Mapper and Artificial Neural Network Classifiers Combined with Landsat TM Imagery Analysis for Obtaining Burnt Area Mapping","volume":"10","author":"Petropoulos","year":"2010","journal-title":"Sensors"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"16917","DOI":"10.3390\/rs71215861","article-title":"Object-Based Urban Tree Species Classification Using Bi-Temporal WorldView-2 and WorldView-3 Images","volume":"7","author":"Li","year":"2015","journal-title":"Remote Sens."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"111354","DOI":"10.1016\/j.rse.2019.111354","article-title":"Auxiliary datasets improve accuracy of object-based land use\/land cover classification in heterogeneous savanna landscapes","volume":"233","author":"Hurskainen","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1016\/0304-3800(92)90003-W","article-title":"Comparing global vegetation maps with the Kappa statistic","volume":"62","author":"Monserud","year":"1992","journal-title":"Ecol. Model."},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Abdi, A.M. (2019). Land cover and land use classification performance of machine learning algorithms in a boreal landscape using Sentinel-2 data. GISci. Remote Sens., 1\u201320.","DOI":"10.1080\/15481603.2019.1650447"},{"key":"ref_92","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_93","first-page":"18","article-title":"Comparison of Random Forest, k-Nearest Neighbor, and Support Vector Machine Classifiers for Land Cover Classification Using Sentinel-2 Imagery","volume":"18","author":"Noi","year":"2018","journal-title":"Sensors"},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"492","DOI":"10.1080\/17538947.2012.748848","article-title":"Evaluation of different machine learning methods for land cover mapping of a Mediterranean area using multi-seasonal Landsat images and Digital Terrain Models","volume":"7","year":"2014","journal-title":"Int. J. Digit. Earth"},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"3137","DOI":"10.1080\/01431160701442120","article-title":"Harshness in image classification accuracy assessment","volume":"29","author":"Foody","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"611","DOI":"10.4236\/ijg.2017.84033","article-title":"Accuracy assessment of land use\/land cover classification using remote sensing and GIS","volume":"8","author":"Rwanga","year":"2017","journal-title":"Int. J. Geosci."},{"key":"ref_97","first-page":"37","article-title":"Land use classification and change detection by using multi-temporal remotely sensed imagery: The case of Chunati wildlife sanctuary, Bangladesh","volume":"21","author":"Islam","year":"2018","journal-title":"Egypt. J. Remote Sens. Space Sci."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"898","DOI":"10.1016\/j.rse.2017.08.035","article-title":"Assessing the accuracy of multi-temporal built-up land layers across rural-urban trajectories in the United States","volume":"204","author":"Leyk","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"2473","DOI":"10.3390\/rs3112473","article-title":"A new approach to change vector analysis using distance and similarity measures","volume":"3","author":"Gillespie","year":"2011","journal-title":"Remote Sens."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"153","DOI":"10.3390\/rs70100153","article-title":"Comparing machine learning classifiers for object-based land cover classification using very high-resolution imagery","volume":"7","author":"Qian","year":"2015","journal-title":"Remote Sens."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"525","DOI":"10.1016\/j.apgeog.2010.11.007","article-title":"A comparison of classification techniques to support land cover and land use analysis in tropical coastal zones","volume":"31","author":"Szuster","year":"2011","journal-title":"Appl. Geogr."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"623","DOI":"10.2747\/1548-1603.49.5.623","article-title":"An evaluation of bagging, boosting, and random forests for land-cover classification in Cape Cod, Massachusetts, USA","volume":"49","author":"Ghimire","year":"2012","journal-title":"GIScience Remote Sens."},{"key":"ref_103","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_104","doi-asserted-by":"crossref","first-page":"1186","DOI":"10.1109\/JSTARS.2014.2313572","article-title":"Comparative Assessment of Supervised Classifiers for Land Use\u2013Land Cover Classification in a Tropical Region Using Time-Series PALSAR Mosaic Data","volume":"7","author":"Shiraishi","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1080\/22797254.2017.1299557","article-title":"Comparison of support vector machine, random forest and neural network classifiers for tree species classification on airborne hyperspectral APEX images","volume":"50","author":"Raczko","year":"2017","journal-title":"Eur. J. Remote Sens."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"012052","DOI":"10.1088\/1755-1315\/20\/1\/012052","article-title":"Comparison of two Classification methods (MLC and SVM) to extract land use and land cover in Johor Malaysia.7th IGRSM International Remote Sensing & GIS Conference and Exhibition, 22\u201323 April 2014, Kuala Lumpur, Malaysia","volume":"20","author":"Deilmai","year":"2014","journal-title":"IOP Conf. Ser. Earth Environ. Sci."},{"key":"ref_107","doi-asserted-by":"crossref","unstructured":"Ahmad, M., Protasov, S., Khan, A.M., Hussain, R., Khattak, A.M., and Khan, W.A. (2018). Fuzziness-based active learning framework to enhance hyperspectral image classification performance for discriminative and generative classifiers. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0188996"},{"key":"ref_108","unstructured":"Lee, R.Y., Ou, D.Y., Shiu, Y.S., and Lei, T.C. (2015, January 24\u201328). Comparisons of using Random Forest and Maximum Likelihood Classifiers with Worldview-2 imagery for classifying Crop Types. Proceedings of the 36th Asian Conference Remote Sensing Foster ACRS, Quezon City, Philippines."},{"key":"ref_109","unstructured":"Abbas, A.W., Ahmad, A., Shah, S., and Saeed, K. (2017, January 10\u201314). Parameter investigation of Artificial Neural Network and Support Vector Machine for image classification. Proceedings of the 14th International Bhurban Conference on Applied Sciences and Technology (IBCAST), Islamabad, Pakistan."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1007\/978-981-13-0680-8_9","article-title":"A Futuristic Deep Learning Framework Approach for Land Use-Land Cover Classification Using Remote Sensing","volume":"Volume 702","author":"Nijhawan","year":"2018","journal-title":"Advanced Computing and Communication Technologies: Proceedings of the 11th ICACCT 2018"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/7\/1135\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:14:54Z","timestamp":1760174094000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/7\/1135"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,4,2]]},"references-count":110,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2020,4]]}},"alternative-id":["rs12071135"],"URL":"https:\/\/doi.org\/10.3390\/rs12071135","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,4,2]]}}}