Abstract
Change detection based on satellite images acquired from an area at different dates is of widespread interest, according to the increasing number of flood-related disasters. The images help to generate products that support emergency response and flood management at a global scale. In this paper, a novel unsupervised change detection approach based on image fusion is introduced. The approach aims to extract the reliable flood extent from very high-resolution (VHR) bi-temporal images. The method takes an advantage of the spectral distortion that occurs during image fusion process to detect the change areas by flood. To this end, a change candidate image is extracted from the fused image generated with bi-temporal images by considering a local spectral distortion. This can be done by employing a universal image quality index (UIQI), which is a measure for local evaluation of spectral distortion. The decision threshold for the determination of changed pixels is set by applying a probability mixture model to the change candidate image based on expectation maximization (EM) algorithm. We used bi-temporal KOMPSAT-2 satellite images to detect the flooded area in the city of N′djamena in Chad. The performance of the proposed method was visually and quantitatively compared with existing change detection methods. The results showed that the proposed method achieved an overall accuracy (OA = 75.04) close to that of the support vector machine (SVM)-based supervised change detection method. Moreover, the proposed method showed a better performance in differentiating the flooded area and the permanent water body compared to the existing change detection methods.
Highlights
Catastrophic events such as floods, landslides, and tsunamis have a significant impact on our lives as these events cause major losses to life and properties
Unlike classical unsupervised change detection methods, which are generally fulfilled based on the difference images, our approach is based on analysis of spectral distortion that occurs during image fusion process
We proposed a novel unsupervised change detection methodology based on a combination of image fusion and spectral distortion measure for the flood extent extraction
Summary
Catastrophic events such as floods, landslides, and tsunamis have a significant impact on our lives as these events cause major losses to life and properties. A threshold for indicating changed areas needs to be determined on the magnitude of the change vector This method performed better in a comparative evaluation of some change detection techniques for detecting flood extent using Landsat TM data [19]. The method can concentrate all spectral variations associated with land cover changes between two acquisition times into a few resulting MAD components They provide an optimal change indicator for multi-temporal remotely sensed images in theory [20]. Since the pixels are not spatially independent and the noise pixels have a great impact on change detection, differences based on spectral feature may fail to reveal the changes in VHR bi-temporal satellite images.
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