Liu Yang, Dai Qin, Liu Jianbo, Liu ShiBin, Yang Jin
Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijinng, China ; University of Chinese Academy of Sciences, Beijing, China.
Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijinng, China.
PLoS One. 2014 Feb 4;9(2):e87480. doi: 10.1371/journal.pone.0087480. eCollection 2014.
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.
利用遥感数据提取烧伤疤痕是精确评估烧伤面积和测量植被恢复情况的有效方法。传统的烧伤疤痕提取方法对边缘模糊且不规则的烧伤疤痕图像效果不佳。为了解决这些问题，本文提出了一种基于水平集方法（LSM）的烧伤疤痕自动提取方法。该方法利用了遥感图像中不同特征的优势，并考虑了快速自动提取烧伤疤痕的实际需求。该方法整合了变化向量分析（CVA）、归一化植被指数（NDVI）和归一化烧伤比（NBR）以获得差异图像，并使用一种新的初始曲线修改传统的水平集方法Chan-Vese（C-V）模型，该初始曲线来自于对两个近红外波段图像的拟合误差应用K均值方法得到的二值图像。利用Landsat 5 TM和Landsat 8 OLI数据集对所提方法进行验证。与传统C-V模型、OSTU算法、模糊C均值（FCM）算法进行比较，结果表明所提方法能够有效、准确地提取火烧烧伤疤痕的轮廓曲线。该方法比传统C-V模型具有更高的提取精度和更低的算法复杂度。
