Abstract
Change-detection analysis using bitemporal satellite imagery is a reliable method for providing and assessing information about flood-induced changes over a wide area in a timely and cost-effective manner. Accurate radiometric normalization between bitemporal imagery is a critical component in the application of change-detection techniques to flood mapping because the accuracy of the change detection is directly affected by the quality of radiometric normalization. A methodology based on multivariate alteration detection (MAD) is introduced as an approach that enables reliable radiometric normalization of bitemporal very high-resolution (VHR) images for detecting flood-induced changes. The method uses a weighting function to adaptively identify weights based on open water features, which are estimated by the normalized difference water index, in the computation of the covariance matrices of the MAD transform. To quantitatively evaluate and test the performance of the proposed method, a comparison is made between it and the iteratively reweighted (IR)-MAD method based on statistical tests and the accuracy of flood change detection. Change vector analysis- and MAD-based change-detection methods were used for the comparison of the proposed and IR-MAD methods. Experimental results on KOMPSAT-2 bitemporal VHR images prove that the proposed method produced better results than the IR-MAD method in the statistical tests and also increased the overall accuracy of flood change detection by 1.8% and 12.6% for the two study sites.
Highlights
Flooding events are the most common natural disaster worldwide, and their frequency may increase in the future due to global climate change;[1] flood monitoring is a national policy issue of increasing importance, requiring rapid access to accurate information that identifies changes induced by floods
Downloaded From: https://www.spiedigitallibrary.org/journals/Journal-of-Applied-Remote-Sensing on 08 Nov 2021 Terms of Use: https://www.spiedigitallibrary.org/terms-of-use in Fig. 3, the number of the extracted pseudoinvariant features (PIFs) using the iteratively reweighted (IR)-multivariate alteration detection (MAD) method is smaller than that of the proposed method, and the majority of extracted PIFs using the IR-MAD method are located in the area affected by flooding, which are unsuitable for radiometric normalization
Because the significance of the statistical evaluation using the paired t-test and F-tests crucially depends on the test pixels, it is difficult to conclude, using these tests alone, that the proposed method is better than the IR-MAD method
Summary
Flooding events are the most common natural disaster worldwide, and their frequency may increase in the future due to global climate change;[1] flood monitoring is a national policy issue of increasing importance, requiring rapid access to accurate information that identifies changes induced by floods. Multitemporal remote sensing imagery has proven useful in addressing computer-assisted change-detection applications related to flood monitoring.[2,3,4] To precisely extract the flood extent information from multitemporal satellite imagery, it is necessary to carry out radiometric correction, which minimizes the unfavorable impact of radiometric differences on change detection caused by variations in imaging conditions. Two types of radiometric corrections, absolute and relative, are commonly employed to normalize remotesensing images for the comparison of multitemporal satellite images.[5] The absolute radiometric correction extracts the absolute reflectance of scene targets at the time of data acquisition.
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