Abstract
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.
Highlights
Change detection techniques using satellite remote sensing images are methods to quantitatively analyze changes occurring to a targeted area based on data obtained for two different points in time [1,2,3,4]
The common form for linear radiometric normalization is given by Equation (1), yNi = ai xi + bi where xi is the digital number of band i in subject image; yNi is the digital number of band i in the normalized subject image; and ai, bi are normalization constants for band i
The mean R2 values of Mean-Standard Deviation (MS) regression, Simple Regression (SR), and NC regression were improved by 0.12%, 0.01%, and 0.13%, respectively, and the mean root mean squared error (RMSE) values were improved by 25.61%, 33.22%, and 24.93%, respectively, relative to the raw subject images
Summary
Change detection techniques using satellite remote sensing images are methods to quantitatively analyze changes occurring to a targeted area based on data obtained for two different points in time [1,2,3,4]. Absolute radiometric normalization often presents difficulties with atmospheric data collection in terms of cost and accessibility, and cannot be implemented when there are no ground-measured values to obtain the detection data; in most cases, relative radiometric normalization is utilized [10,12]. Relative radiometric normalization does not require complex atmospheric transfer parameterization processes and can yield information on the relative changes contained in multi-temporal image data that are tracked and corrected so that areas with changes can be identified and quickly [12]. One commonly used method is a mathematical model wherein data are corrected through regression equations, assuming a linear relationship between bands in multi-temporal image data, provided that the area is the same [13,14]. Conventional relative radiometric normalization methods assume that radiometric relationships between the subject image and reference image are linear.
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