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Remote Sens. 2017, 9(11), 1163;

Generation of Radiometric, Phenological Normalized Image Based on Random Forest Regression for Change Detection

Department of Smart ICT Convergence, Konkuk University, Seoul 05029, Korea
Department of Civil and Environmental Engineering, Seoul National University, Seoul 08826, Korea
Department of Advanced Technology Fusion, Konkuk University, Seoul 05029, Korea
Agency for Defense Development, Daejeon 34060, Korea
Author to whom correspondence should be addressed.
Received: 21 July 2017 / Revised: 10 November 2017 / Accepted: 10 November 2017 / Published: 13 November 2017
(This article belongs to the Special Issue Uncertainty in Remote Sensing Image Analysis)
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Efforts have been made to detect both naturally occurring and anthropogenic changes to the Earth’s 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. View Full-Text
Keywords: random forest regression; relative radiometric normalization; nonlinear; scattergram; change detection random forest regression; relative radiometric normalization; nonlinear; scattergram; change detection

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Seo, D.K.; Kim, Y.H.; Eo, Y.D.; Park, W.Y.; Park, H.C. Generation of Radiometric, Phenological Normalized Image Based on Random Forest Regression for Change Detection. Remote Sens. 2017, 9, 1163.

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