{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T16:58:36Z","timestamp":1772038716264,"version":"3.50.1"},"reference-count":28,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2016,4,11]],"date-time":"2016-04-11T00:00:00Z","timestamp":1460332800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Rural Development Administration, Republic of Korea","award":["PJ00997803"],"award-info":[{"award-number":["PJ00997803"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Traditional classification accuracy assessments based on summary statistics from a confusion matrix furnish a global (location invariant) view of classification accuracy. To estimate the spatial distribution of classification accuracy, a geostatistical integration approach is presented in this paper. Indicator kriging with local means is combined with logistic regression to integrate an image-derived ambiguity index with classification accuracy values at reference data locations. As for the ambiguity measure, a novel discrimination capability index (DCI) is defined from per class posteriori probabilities and then calibrated via logistic regression to derive soft probabilities. Integration of indicator-coded reference data with soft probabilities is finally carried out for mapping classification accuracy. It is demonstrated via a case study involving classification of multi-temporal and multi-sensor SAR datasets, that the proposed approach can provide a map of locally-varying accuracy values, while respecting the overall accuracy derived from the confusion matrix. It can also highlight areas where the benefit of data fusion was significant. It is expected that the indicator approach presented in this paper could be a useful methodology for assessing the spatial quality of classification results in a probabilistic way.<\/jats:p>","DOI":"10.3390\/rs8040320","type":"journal-article","created":{"date-parts":[[2016,4,11]],"date-time":"2016-04-11T12:07:36Z","timestamp":1460376456000},"page":"320","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Spatial Estimation of Classification Accuracy Using Indicator Kriging with an Image-Derived Ambiguity Index"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9778-3624","authenticated-orcid":false,"given":"No-Wook","family":"Park","sequence":"first","affiliation":[{"name":"Department of Geoinformatic Engineering, Inha Univeristy, Incheon 22212, Korea"}]},{"given":"Phaedon","family":"Kyriakidis","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering and Geomatics, Cyprus University of Technology, Limassol 3036, Cyprus"}]},{"given":"Suk-Young","family":"Hong","sequence":"additional","affiliation":[{"name":"National Institute of Agricultural Sciences, Rural Development Administration, Wanju-gun 55365, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2016,4,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1477","DOI":"10.1080\/01431160412331331012","article-title":"Application of logistic regression model and its validation for landslide susceptibility mapping using GIS and remote sensing","volume":"26","author":"Lee","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1016\/j.rse.2005.03.015","article-title":"Application of MODIS derived parameters for regional crop yield assessment","volume":"97","author":"Doraiswamy","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"7561","DOI":"10.1016\/j.atmosenv.2008.05.057","article-title":"A review of land-use regression models to assess spatial variation of outdoor air pollution","volume":"42","author":"Hoek","year":"2008","journal-title":"Atmos. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Heuvelink, G.B.M. (1998). Error Propagation in Environmental Modeling with GIS, Taylor & Francis.","DOI":"10.4324\/9780203016114"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1316","DOI":"10.1109\/36.763295","article-title":"Multisensor data fusion using fuzzy concepts: Application to land-cover classification using ERS-1\/JERS-1 SAR composites","volume":"37","author":"Solaiman","year":"1999","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1350","DOI":"10.1109\/36.763299","article-title":"A neural-statistical approach to multitemporal and multisource remote-sensing image classification","volume":"37","author":"Bruzzone","year":"1999","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2291","DOI":"10.1109\/TGRS.2002.802476","article-title":"Multiple classifiers applied to multisource remote sensing data","volume":"40","author":"Briem","year":"2002","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1016\/j.patrec.2005.08.011","article-title":"Random forests for land cover classification","volume":"27","author":"Gislason","year":"2006","journal-title":"Pattern Recogn. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"3858","DOI":"10.1109\/TGRS.2007.898446","article-title":"Fusion of support vector machines for classification of multisensory data","volume":"45","author":"Waske","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1016\/j.rse.2010.10.005","article-title":"An artificial immune network approach to multi-sensor land use\/cover classification","volume":"115","author":"Gong","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2734","DOI":"10.1109\/TGRS.2012.2211882","article-title":"Combining support vector machines and Markov random fields in an integrated framework for contextual image classification","volume":"51","author":"Moser","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"5795","DOI":"10.3390\/rs6065795","article-title":"Spectral-spatial classification of hyperspectral image based on kernel extreme learning machine","volume":"6","author":"Chen","year":"2014","journal-title":"Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"922","DOI":"10.3390\/rs70100922","article-title":"Object-based crop species classification based on the combination of airborne hyperspectral images and LiDAR data","volume":"7","author":"Liu","year":"2015","journal-title":"Remote Sens."},{"key":"ref_14","unstructured":"Lillesand, T., Kiefer, R.W., and Chipman, J. (2007). Remote Sensing and Image Interpretation, Wiley. [6th ed.]."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Congalton, R.G., and Green, K. (2008). Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, CRC Press. [2nd ed.].","DOI":"10.1201\/9781420055139"},{"key":"ref_16","first-page":"1195","article-title":"Measuring uncertainty in class assignment for natural resource maps under fuzzy logic","volume":"63","author":"Zhu","year":"1997","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1016\/S0034-4257(98)00061-3","article-title":"Estimation and mapping of misclassification probabilities for thematic land cover mapping","volume":"66","author":"Steele","year":"1998","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1023\/A:1012778302005","article-title":"A geostatistical approach for mapping thematic classification accuracy and evaluating the impact of inaccurate spatial data on ecological model predictions","volume":"8","author":"Kyriakidis","year":"2001","journal-title":"Environ. Ecol. Stat."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1007\/BF01031292","article-title":"Non-parametric estimation of spatial distribution","volume":"15","author":"Journel","year":"1983","journal-title":"Math. Geol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"4667","DOI":"10.1080\/01431160801947341","article-title":"Integration of multitemporal\/polarization C-band SAR data sets for land-cover classification","volume":"29","author":"Park","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_21","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":"2004","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1233","DOI":"10.1080\/01431169608949079","article-title":"Classification of remotely-sensed imagery using an indicator kriging approach: Application to the problem of calcite-dolomite mineral mapping","volume":"17","year":"1996","journal-title":"Int. J. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1007\/s101090100077","article-title":"Geostatistical incorporation of spatial corrdinates into supervised classification of hyperspectral data","volume":"4","author":"Goovaerts","year":"2002","journal-title":"J. Geograph. Syst."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"4046","DOI":"10.1109\/TGRS.2013.2279118","article-title":"A feature-space indicator kriging approach for remote sensing image classification","volume":"52","author":"Chiang","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Hilbe, J.M. (2009). Logistic Regression Models, CRC Press.","DOI":"10.1201\/9781420075779"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Goovaerts, P. (1997). Geostatistics for Natural Resources Evaluation, Oxford University Press.","DOI":"10.1093\/oso\/9780195115383.001.0001"},{"key":"ref_27","unstructured":"Deutsch, C.V., and Journel, A.G. (1998). GSLIB: Geostatistical Software Library and User\u2019s Guide, Oxford University Press. [2nd ed.]."},{"key":"ref_28","unstructured":"Sherrod, P.H. DTREG Predictive Modeling Software. Available online: http:\/\/www.dtreg.com."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/8\/4\/320\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T19:22:03Z","timestamp":1760210523000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/8\/4\/320"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,4,11]]},"references-count":28,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2016,4]]}},"alternative-id":["rs8040320"],"URL":"https:\/\/doi.org\/10.3390\/rs8040320","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,4,11]]}}}