An approach for flood inundated duration extraction based on Level Set Method using remote sensing data

Flood information extraction, includes flooded area and inundated duration, using remote sensing data plays a fundamental role on evaluating loss and conducting disaster rescue and relief. However, the clear-sky image is restricted by different weather conditions such as cloud, rain, fog and so on. Consequently, lack of the critical period remote sensing data of flood routing cannot produce inundated duration information timely and rapidly once the hazards happen. In order to overcome the above limitation, a new gapless spatio-temporal shape evolving method for extracting flooded inundated duration is presented in this paper. The emphasis of this study lies in applying Level Set Method (LSM) to insert a series of intermediate surface between bi-temporal images without predefined information. Level Set Method is a better choice because it can handle topology changes to extract object with variable shapes from image. Besides, the proposed method modifies the finite difference model both in space and time to speed up the iteration and improve extraction precision. This paper selects GF-1 images to validate the methodology. The result indicated that the proposed method provides more accurate and less error information when compared with official statistic.