{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T20:28:15Z","timestamp":1783110495583,"version":"3.54.6"},"reference-count":63,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2017,11,13]],"date-time":"2017-11-13T00:00:00Z","timestamp":1510531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Efforts have been made to detect both naturally occurring and anthropogenic changes to the Earth\u2019s surface by using satellite remote sensing imagery. There is a need to maintain the homogeneity of radiometric and phenological conditions to ensure accuracy in change detection, but images to assess long-term changes in time-series data that satisfy such conditions are difficult to obtain. For this reason, image normalization is essential. In particular, the normalizing compositive conditions require nonlinear modeling, and random forest (RF) techniques can be utilized for this normalization. This study employed Landsat-5 Thematic Mapper satellite images with temporal, radiometric and phenological differences, and obtained Radiometric Control Set Samples by selecting no-change pixels between the subject image and reference image using scattergrams. In the obtained no-change regions, RF regression was modeled, and normalized images were obtained. Next, normalization performance was evaluated by comparing the results against the following conventional linear regression methods: mean-standard deviation regression, simple regression, and no-change regression. The normalization performance of RF regression was much higher. In addition, for an additional usefulness evaluation in normalization, the normalization performance was compared with other nonlinear ensemble regressions, i.e. Adaptive Boosting regression and Stochastic Gradient Boosting regression, which confirmed that the normalization performance of RF regression was significantly higher. In other words, it was found to be highly useful for normalization when compared to other nonlinear ensemble regressions. Finally, as a result of performing change detection, normalized subject images generated by RF regression showed the highest accuracy, which indicated that the proposed method (where the image was normalized using RF regression) may be useful in change detection between multi-temporal image datasets.<\/jats:p>","DOI":"10.3390\/rs9111163","type":"journal-article","created":{"date-parts":[[2017,11,13]],"date-time":"2017-11-13T11:12:36Z","timestamp":1510571556000},"page":"1163","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":79,"title":["Generation of Radiometric, Phenological Normalized Image Based on Random Forest Regression for Change Detection"],"prefix":"10.3390","volume":"9","author":[{"given":"Dae","family":"Seo","sequence":"first","affiliation":[{"name":"Department of Smart ICT Convergence, Konkuk University, Seoul 05029, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8200-9639","authenticated-orcid":false,"given":"Yong","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, Seoul National University, Seoul 08826, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Eo","sequence":"additional","affiliation":[{"name":"Department of Advanced Technology Fusion, Konkuk University, Seoul 05029, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wan","family":"Park","sequence":"additional","affiliation":[{"name":"Agency for Defense Development, Daejeon 34060, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hyun","family":"Park","sequence":"additional","affiliation":[{"name":"Agency for Defense Development, Daejeon 34060, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2017,11,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"122","DOI":"10.3390\/rs1030122","article-title":"Similarity Measures of Remotely Sensed Multi-Sensor Images for Change Detection Applications","volume":"1","author":"Alberga","year":"2009","journal-title":"Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1508","DOI":"10.3390\/rs2061508","article-title":"Change Detection Accuracy and Image Properties: A Study Using Simulated Data","volume":"2","author":"Almutairi","year":"2010","journal-title":"Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"989","DOI":"10.1080\/01431168908903939","article-title":"Digital Change Detection Technique Using Remotely-Sensed Data","volume":"10","author":"Singh","year":"1989","journal-title":"Int. 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