An automatic method for flooded area extraction based on level set method using remote sensing data

Flooded area extraction using remote sensing data plays a fundamental role on precisely evaluating flooded area and conducting disaster rescue and relief. The flooded area images may have irregular and fuzzy outline. The traditional methods are easily affected by difference image with low contrast obtained by multi-temporal remote sensing images. In order to overcome the above limitations, a new automatic and un-supervised method for extracting flooded area is presented in this paper. The emphasis of this study lies in creating high contrast difference image via weighted combing different features and applying Level Set Method (LSM) to extract flooded area without predefined information. LSM Chan-Vese (C-V) model is a better choice because it can handle topology changes to extract object with variable shapes from image. Besides, the proposed method modifies the initial curve of C-V model to speed up the iteration and improve extraction precision. This paper selects Landsat 8 OLI data set to validate the methodology this paper study. The proposed method provides more accurate and efficient extraction of flooded area extent when compared with Fuzzy C-Mean (FCM) algorithm.