{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T18:13:19Z","timestamp":1783707199271,"version":"3.55.0"},"reference-count":69,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2017,12,7]],"date-time":"2017-12-07T00:00:00Z","timestamp":1512604800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 41631176"],"award-info":[{"award-number":["No. 41631176"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Land Use and Land Cover (LULC) classification is vital for environmental and ecological applications. Sentinel-2 is a new generation land monitoring satellite with the advantages of novel spectral capabilities, wide coverage and fine spatial and temporal resolutions. The effects of different spatial resolution unification schemes and methods on LULC classification have been scarcely investigated for Sentinel-2. This paper bridged this gap by comparing the differences between upscaling and downscaling as well as different downscaling algorithms from the point of view of LULC classification accuracy. The studied downscaling algorithms include nearest neighbor resampling and five popular pansharpening methods, namely, Gram-Schmidt (GS), nearest neighbor diffusion (NNDiffusion), PANSHARP algorithm proposed by Y. Zhang, wavelet transformation fusion (WTF) and high-pass filter fusion (HPF). Two spatial features, textural metrics derived from Grey-Level-Co-occurrence Matrix (GLCM) and extended attribute profiles (EAPs), are investigated to make up for the shortcoming of pixel-based spectral classification. Random forest (RF) is adopted as the classifier. The experiment was conducted in Xitiaoxi watershed, China. The results demonstrated that downscaling obviously outperforms upscaling in terms of classification accuracy. For downscaling, image sharpening has no obvious advantages than spatial interpolation. Different image sharpening algorithms have distinct effects. Two multiresolution analysis (MRA)-based methods, i.e., WTF and HFP, achieve the best performance. GS achieved a similar accuracy with NNDiffusion and PANSHARP. Compared to image sharpening, the introduction of spatial features, both GLCM and EAPs can greatly improve the classification accuracy for Sentinel-2 imagery. Their effects on overall accuracy are similar but differ significantly to specific classes. In general, using the spectral bands downscaled by nearest neighbor interpolation can meet the requirements of regional LULC applications, and the GLCM and EAPs spatial features can be used to obtain more precise classification maps.<\/jats:p>","DOI":"10.3390\/rs9121274","type":"journal-article","created":{"date-parts":[[2017,12,7]],"date-time":"2017-12-07T11:49:10Z","timestamp":1512647350000},"page":"1274","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":95,"title":["Performance Evaluation of Downscaling Sentinel-2 Imagery for Land Use and Land Cover Classification by Spectral-Spatial Features"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1284-285X","authenticated-orcid":false,"given":"Hongrui","family":"Zheng","sequence":"first","affiliation":[{"name":"Key Laboratory for Satellite Mapping Technology and Applications of National Administration of Surveying, Mapping and Geoinformation of China, Nanjing 210023, China"},{"name":"Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing 210023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peijun","family":"Du","sequence":"additional","affiliation":[{"name":"Key Laboratory for Satellite Mapping Technology and Applications of National Administration of Surveying, Mapping and Geoinformation of China, Nanjing 210023, China"},{"name":"Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing 210023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1929-987X","authenticated-orcid":false,"given":"Jike","family":"Chen","sequence":"additional","affiliation":[{"name":"Key Laboratory for Satellite Mapping Technology and Applications of National Administration of Surveying, Mapping and Geoinformation of China, Nanjing 210023, China"},{"name":"Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing 210023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junshi","family":"Xia","sequence":"additional","affiliation":[{"name":"Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo 113-8654, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Erzhu","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory for Satellite Mapping Technology and Applications of National Administration of Surveying, Mapping and Geoinformation of China, Nanjing 210023, China"},{"name":"Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing 210023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhigang","family":"Xu","sequence":"additional","affiliation":[{"name":"Key Laboratory for Satellite Mapping Technology and Applications of National Administration of Surveying, Mapping and Geoinformation of China, Nanjing 210023, China"},{"name":"Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing 210023, China"},{"name":"School of Resource Engineering, Longyan University, Longyan 364012, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaojuan","family":"Li","sequence":"additional","affiliation":[{"name":"Collaborative Innovation Center for the South China Sea Studies, Nanjing University, Nanjing 210023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7321-4590","authenticated-orcid":false,"given":"Naoto","family":"Yokoya","sequence":"additional","affiliation":[{"name":"Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo 113-8654, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2017,12,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1093","DOI":"10.1080\/014311600210092","article-title":"Land cover mapping of large areas from satellites: Status and research priorities","volume":"21","author":"Cihlar","year":"2000","journal-title":"Int. J. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1016\/S0305-9006(03)00066-7","article-title":"Remote sensing technology for mapping and monitoring land-cover and land-use change","volume":"61","author":"Rogan","year":"2004","journal-title":"Prog. Plan."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Wang, Q., Blackburn, G.A., Onojeghuo, A.O., Dash, J., Zhou, L., Zhang, Y., and Atkinson, P.M. (2017). Fusion of landsat 8 oli and sentinel-2 MSI data. IEEE Trans. Geosci. Remote Sens.","DOI":"10.1109\/TGRS.2017.2683444"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3220","DOI":"10.1109\/TGRS.2013.2271813","article-title":"Empirical estimation of leaf chlorophyll density in winter wheat canopies using sentinel-2 spectral resolution","volume":"52","author":"Vincini","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Fernandes, R., Weiss, M., Camacho, F., Berthelot, B., Baret, F., and Duca, R. (2014, January 13\u201318). Development and assessment of leaf area index algorithms for the sentinel-2 multispectral imager. Proceedings of the 2014 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Quebec City, QC, Canada.","DOI":"10.1109\/IGARSS.2014.6947342"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Toming, K., Kutser, T., Laas, A., Sepp, M., Paavel, B., and N\u00f5ges, T. (2016). First experiences in mapping lake water quality parameters with sentinel-2 MSI imagery. Remote Sens., 8.","DOI":"10.3390\/rs8080640"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Du, Y., Zhang, Y., Ling, F., Wang, Q., Li, W., and Li, X. (2016). Water bodies\u2019 mapping from sentinel-2 imagery with modified normalized difference water index at 10-m spatial resolution produced by sharpening the swir band. Remote Sens., 8.","DOI":"10.3390\/rs8040354"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1080\/22797254.2017.1297540","article-title":"Object-based water body extraction model using sentinel-2 satellite imagery","volume":"50","author":"Kaplan","year":"2017","journal-title":"Eur. J. Remote Sens."},{"key":"ref_9","first-page":"403","article-title":"Performance evaluation of object based greenhouse detection from sentinel-2 MSI and landsat 8 oli data: A case study from almer\u00eda (Spain)","volume":"52","author":"Novelli","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Pesaresi, M., Corbane, C., Julea, A., Florczyk, A.J., Syrris, V., and Soille, P. (2016). Assessment of the added-value of sentinel-2 for detecting built-up areas. Remote Sens., 8.","DOI":"10.3390\/rs8040299"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Immitzer, M., Vuolo, F., and Atzberger, C. (2016). First experience with sentinel-2 data for crop and tree species classifications in central europe. Remote Sens., 8.","DOI":"10.3390\/rs8030166"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.isprsjprs.2015.10.005","article-title":"Examining the potential of sentinel-2 MSI spectral resolution in quantifying above ground biomass across different fertilizer treatments","volume":"110","author":"Sibanda","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/j.rse.2015.06.003","article-title":"Downscaling modis images with area-to-point regression kriging","volume":"166","author":"Wang","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1016\/S0034-4257(02)00127-X","article-title":"Lithologic mapping in the mountain pass, california area using advanced spaceborne thermal emission and reflection radiometer (ASTER) data","volume":"84","author":"Rowan","year":"2003","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1309","DOI":"10.1016\/j.jseaes.2011.07.017","article-title":"Identification of hydrothermal alteration minerals for exploring of porphyry copper deposit using ASTER data, se iran","volume":"42","author":"Pour","year":"2011","journal-title":"J. Asian Earth Sci."},{"key":"ref_16","first-page":"106","article-title":"Downscaling in remote sensing","volume":"22","author":"Atkinson","year":"2013","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1109\/TMI.1983.4307610","article-title":"Comparison of interpolating methods for image resampling","volume":"2","author":"Parker","year":"1983","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1123","DOI":"10.1080\/01431169408954146","article-title":"Investigation of image resampling effects upon the textural information content of a high spatial resolution remotely sensed image","volume":"15","author":"Roy","year":"1994","journal-title":"Int. J. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1109\/LGRS.2007.908884","article-title":"Improving modis spatial resolution for snow mapping using wavelet fusion and arsis concept","volume":"5","author":"Sirguey","year":"2008","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_20","unstructured":"Laben, C.A., and Brower, B.V. (2000). Process for Enhancing the Spatial Resolution of Multispectral Imagery Using Pan-Sharpening. (6,011,875), U.S. Patent."},{"key":"ref_21","first-page":"691","article-title":"Fusion of satellite images of different spatial resolutions: Assessing the quality of resulting images","volume":"63","author":"Wald","year":"1997","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1186\/1687-6180-2011-79","article-title":"A survey of classical methods and new trends in pansharpening of multispectral images","volume":"2011","author":"Amro","year":"2011","journal-title":"EURASIP J. Adv. Signal Process."},{"key":"ref_23","first-page":"657","article-title":"Understanding image fusion","volume":"70","author":"Zhang","year":"2004","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"823","DOI":"10.1080\/014311698215748","article-title":"Review article multisensor image fusion in remote sensing: Concepts, methods and applications","volume":"19","author":"Pohl","year":"1998","journal-title":"Int. J. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3012","DOI":"10.1109\/TGRS.2007.904923","article-title":"Comparison of pansharpening algorithms: Outcome of the 2006 grs-s data-fusion contest","volume":"45","author":"Alparone","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","first-page":"301","article-title":"Merging multiresolution spot hrv and landsat tm data","volume":"53","author":"Welch","year":"1987","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_27","first-page":"305","article-title":"Comparison of fusion methods for generating 250 m modis image","volume":"26","author":"Kim","year":"2010","journal-title":"Korean J. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/j.compag.2016.12.006","article-title":"Effect of pan-sharpening multi-temporal landsat 8 imagery for crop type differentiation using different classification techniques","volume":"134","author":"Gilbertson","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/j.rse.2016.10.030","article-title":"Fusion of sentinel-2 images","volume":"187","author":"Wang","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Pereira, M.J., Ramos, A., Nunes, R., Azevedo, L., and Soares, A. (2016, January 15\u201317). Geostatistical data fusion: Application to red edge bands of sentinel 2. Proceedings of the 2016 International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, NV, USA.","DOI":"10.1109\/CSCI.2016.0147"},{"key":"ref_31","first-page":"723","article-title":"Pansharpening on the narrow vnir and swir spectral bands of sentinel-2","volume":"XLI-B7","author":"Vaiopoulos","year":"2016","journal-title":"ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_32","first-page":"555","article-title":"Object-oriented image processing in an integrated gis\/remote sensing environment and perspectives for environmental applications","volume":"2","author":"Blaschke","year":"2000","journal-title":"Environ. Inf. Plan. Politics Public"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"4039","DOI":"10.1080\/01431160600702632","article-title":"Comparison of pixel-based and object-oriented image classification approaches\u2014A case study in a coal fire area, Wuda, Inner Mongolia, China","volume":"27","author":"Yan","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"856","DOI":"10.1109\/JSTSP.2012.2208177","article-title":"Classification of remote sensing optical and lidar data using extended attribute profiles","volume":"6","author":"Pedergnana","year":"2012","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.isprsjprs.2015.03.002","article-title":"Random forest and rotation forest for fully polarized SAR image classification using polarimetric and spatial features","volume":"105","author":"Du","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1109\/TGRS.1990.572937","article-title":"Evaluation of the grey-level co-occurrence matrix method for land-cover classification using spot imagery","volume":"28","author":"Marceau","year":"1990","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/S0924-2716(98)00027-6","article-title":"Optimisation of building detection in satellite images by combining multispectral classification and texture filtering","volume":"54","author":"Zhang","year":"1999","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/0034-4257(92)90011-8","article-title":"A comparison of spatial feature extraction algorithms for land-use classification with spot hrv data","volume":"40","author":"Gong","year":"1992","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"733","DOI":"10.1080\/01431160512331316838","article-title":"The utility of texture analysis to improve per-pixel classification for high to very high spatial resolution imagery","volume":"26","author":"Puissant","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1940","DOI":"10.1109\/TGRS.2003.814625","article-title":"Classification and feature extraction for remote sensing images from urban areas based on morphological transformations","volume":"41","author":"Benediktsson","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1109\/TGRS.2004.842478","article-title":"Classification of hyperspectral data from urban areas based on extended morphological profiles","volume":"43","author":"Benediktsson","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"5975","DOI":"10.1080\/01431161.2010.512425","article-title":"Extended profiles with morphological attribute filters for the analysis of hyperspectral data","volume":"31","author":"Waske","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"3747","DOI":"10.1109\/TGRS.2010.2048116","article-title":"Morphological attribute profiles for the analysis of very high resolution images","volume":"48","author":"Benediktsson","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"542","DOI":"10.1109\/LGRS.2010.2091253","article-title":"Classification of hyperspectral images by using extended morphological attribute profiles and independent component analysis","volume":"8","author":"Villa","year":"2011","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"2147","DOI":"10.1109\/JSTARS.2014.2298876","article-title":"Automatic framework for spectral-spatial classification based on supervised feature extraction and morphological attribute profiles","volume":"7","author":"Ghamisi","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"5771","DOI":"10.1109\/TGRS.2013.2292544","article-title":"Automatic spectral-spatial classification framework based on attribute profiles and supervised feature extraction","volume":"52","author":"Ghamisi","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1007\/s00267-009-9393-6","article-title":"A drinking water crisis in lake taihu, china: Linkage to climatic variability and lake management","volume":"45","author":"Qin","year":"2010","journal-title":"Environ. Manag."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"510","DOI":"10.1016\/j.orggeochem.2011.03.007","article-title":"Spatial-seasonal dynamics of chromophoric dissolved organic matter in lake taihu, a large eutrophic, shallow lake in china","volume":"42","author":"Zhang","year":"2011","journal-title":"Org. Geochem."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jenvman.2013.11.035","article-title":"Inferring land use and land cover impact on stream water quality using a bayesian hierarchical modeling approach in the Xitiaoxi River Watershed, China","volume":"133","author":"Wan","year":"2014","journal-title":"J. Environ. Manag."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.rse.2011.11.026","article-title":"Sentinel-2: Esa\u2019s optical high-resolution mission for gmes operational services","volume":"120","author":"Drusch","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_51","unstructured":"Muller-Wilm, U., Louis, J., Richter, R., Gascon, F., and Niezette, M. (2013, January 9\u201313). Sentinel-2 level 2a prototype processor: Architecture, algorithms and first results. Proceedings of the 2013 ESA Living Planet Symposium, Edinburgh, UK."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Zhang, Y., and Mishra, R.K. (2012, January 22\u201327). A review and comparison of commercially available pan-sharpening techniques for high resolution satellite image fusion. Proceedings of the 2012 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Munich, Germany.","DOI":"10.1109\/IGARSS.2012.6351607"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"013107","DOI":"10.1117\/1.OE.53.1.013107","article-title":"Nearest-neighbor diffusion-based pan-sharpening algorithm for spectral images","volume":"53","author":"Sun","year":"2014","journal-title":"Opt. Eng."},{"key":"ref_54","first-page":"214","article-title":"Multispectral multisensor image fusion using wavelet transforms","volume":"3716","author":"Lemeshewsky","year":"1999","journal-title":"Proc. SPIE Int. Soc. Opt. Eng."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1107","DOI":"10.14358\/PERS.74.9.1107","article-title":"Optimizing the high-pass filter addition technique for image fusion","volume":"74","author":"Gangkofner","year":"2008","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1204","DOI":"10.1109\/36.763274","article-title":"Multiresolution-based image fusion with additive wavelet decomposition","volume":"37","author":"Nunez","year":"1999","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"2300","DOI":"10.1109\/TGRS.2002.803623","article-title":"Context-driven fusion of high spatial and spectral resolution images based on oversampled multiresolution analysis","volume":"40","author":"Aiazzi","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","article-title":"Textural features for image classification","volume":"3","author":"Haralick","year":"1973","journal-title":"Syst. Man Cybern."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forest","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_60","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_61","doi-asserted-by":"crossref","unstructured":"Gomariz-Castillo, F., Alonso-Sarr\u00eda, F., and C\u00e1novas-Garc\u00eda, F. (2017). Improving classification accuracy of multi-temporal landsat images by assessing the use of different algorithms, textural and ancillary information for a mediterranean semiarid area from 2000 to 2015. Remote Sens., 9.","DOI":"10.3390\/rs9101058"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"2225","DOI":"10.1016\/j.patrec.2010.03.014","article-title":"Variable selection using random forests","volume":"31","author":"Genuer","year":"2010","journal-title":"Pattern Recognit. Lett."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/3-540-45014-9_1","article-title":"Ensemble methods in machine learning","volume":"1857","author":"Dietterich","year":"2000","journal-title":"Mult. Classif. Syst."},{"key":"ref_64","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_65","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/S0034-4257(99)00055-3","article-title":"Coordinating methodologies for scaling landcover classifications from site-specific to global: Steps toward validating global map products","volume":"70","author":"Thomlinson","year":"1999","journal-title":"Remote Sens. Environ."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1385","DOI":"10.1080\/01431168508948285","article-title":"The effects of spatial resolution on the classification of thematic mapper data","volume":"6","author":"Irons","year":"1985","journal-title":"Int. J. Remote Sens."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"3957","DOI":"10.1080\/01431160801891838","article-title":"Integrating airborne SAR, landsat TM and airborne geophysics data for improving geological mapping in the amazon region: The cigano granite, Caraj\u00e1s Province, Brazil","volume":"29","author":"Teruiya","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_68","first-page":"270","article-title":"A review of assessing the accuracy of classification of remotely sensed data","volume":"119","author":"Congalton","year":"1991","journal-title":"Work. Pap."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"3311","DOI":"10.1080\/01431160600649254","article-title":"Influence of image fusion approaches on classification accuracy: A case study","volume":"27","author":"Colditz","year":"2006","journal-title":"Int. J. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/12\/1274\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:53:01Z","timestamp":1760208781000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/9\/12\/1274"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,12,7]]},"references-count":69,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2017,12]]}},"alternative-id":["rs9121274"],"URL":"https:\/\/doi.org\/10.3390\/rs9121274","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,12,7]]}}}