Image Fusion-Based Change Detection for Flood Extent Extraction Using Bi-Temporal Very High-Resolution Satellite Images
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.
Preface: Remote Sensing in Flood Monitoring and Management
This Special Issue is a collection of papers studying the use of remote sensing data and methods for flood monitoring and management. The articles contributed span a wide range of topics and present novel processing techniques, review methods and discuss limitations, and also report on current capabilities and outline emerging needs. This preface provides a brief overview of the content. [...]
Comparison and enhancement of MODIS cloud mask products for Southeast Asia
Accurate quality information on cloud occurrence is of utmost importance for a wide range of remote-sensing applications and analyses. This study compares the two existing cloud mask products available for the Moderate Resolution Imaging Spectroradiometer (MODIS), stored in the quality layer of the MOD09 daily surface reflectance product. For both masks, statistics on cloud occurrence are calculated for 1 year of daily surface reflectance data covering the area of southeast Asia. Furthermore, a cloud mask enhancement algorithm is presented for increasing cloud flag reliability by effectively combining the existing cloud labels together with the utilization of annual statistics based on the blue reflectance band. Furthermore, since a lot of compositing algorithms rely on cloud mask information for the filtering of unsuitable observations, the influence of the different cloud masks on 8-day MOD09A1 composite outputs is examined with respect to data availability and average view angles. The results of the statistical analysis show that the accuracy of the two cloud mask products differs significantly for southeast Asia. Particularly, the radiative influence of land cover proves to strongly affect the reliability of the cloud flags, although to varying degrees throughout the year. The enhancement algorithm successfully identifies undetected clear observations in the original masks while simultaneously setting upper limits for atmospheric contamination. In this manner, considerably higher proportions of cloud-free observations can be retained compared to a clear-sky conservative combination of the masks as applied in other compositing algorithms.