title: Study of burn scar extraction automatically based on level set method using remote sensing data.
abstract: Burn scar extraction using remote sensing data is an efficient way to precisely evaluate burn area and measure vegetation recovery. Traditional burn scar extraction methodologies have no well effect on burn scar image with blurred and irregular edges. To address these issues, this paper proposes an automatic method to extract burn scar based on Level Set Method (LSM). This method utilizes the advantages of the different features in remote sensing images, as well as considers the practical needs of extracting the burn scar rapidly and automatically. This approach integrates Change Vector Analysis (CVA), Normalized Difference Vegetation Index (NDVI) and the Normalized Burn Ratio (NBR) to obtain difference image and modifies conventional Level Set Method Chan-Vese (C-V) model with a new initial curve which results from a binary image applying K-means method on fitting errors of two near-infrared band images. Landsat 5 TM and Landsat 8 OLI data sets are used to validate the proposed method. Comparison with conventional C-V model, OSTU algorithm, Fuzzy C-mean (FCM) algorithm are made to show that the proposed approach can extract the outline curve of fire burn scar effectively and exactly. The method has higher extraction accuracy and less algorithm complexity than that of the conventional C-V model.
authors: Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijinng, China ; University of Chinese Academy of Sciences, Beijing, China.
authors: Yang
authors: Y
authors: Liu
authors: Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijinng, China.
authors: Qin
authors: Q
authors: Dai
authors: Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijinng, China.
authors: Jianbo
authors: J
authors: Liu
authors: Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijinng, China.
authors: ShiBin
authors: S
authors: Liu
authors: Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijinng, China.
authors: Jin
authors: J
authors: Yang
mesh_terms: D000465:Algorithms; D005390:Fires; D005602:France; D016018:Least-Squares Analysis; D058998:Remote Sensing Technology / Q000379:methods*; D013781:Texas; D014861:Washington