Change Detection of Water Index in Danjiangkou Reservoir Using Mixed Log-Normal Distribution Based Active Contour Model

Abstract

The use of synthetic aperture radar (SAR) images for water segmentation can accurately extract the boundaries of water areas and is of great significance for studying the temporal and spatial changes of lakes and other environmental elements. In view of the fact that SAR image itself has characteristics such as speckle noise and large volumes, this paper proposes an edge active contour model (ACM) based on the mixed log-normal distribution for SAR image edge extraction and evaluates the parameters of distribution by using the classic expectation-maximization (EM) algorithm. Furthermore, compared with the existing models, the proposed algorithm introduces regional variable coefficients and modifies the evolution rate in the distance regularization term so that the level set can be quickly and accurately stopped at the target edge. For practical application, the proposed ACM is applied to extract the outline of the Danjiangkou Reservoir (DJKR) with time as a sequence. The experimental results show that it is robust to noise, improves the precision of land and water segmentation, and helps to determine the changing trends of indicators, such as water surface area, average water depth, and relative storage capacity.