globalchange  > 过去全球变化的重建
DOI: 10.1371/journal.pone.0087480
论文题名:
Study of Burn Scar Extraction Automatically Based on Level Set Method using Remote Sensing Data
作者: Yang Liu; Qin Dai; JianBo Liu; ShiBin Liu; Jin Yang
刊名: PLOS ONE
ISSN: 1932-6203
出版年: 2014
发表日期: 2014-2-4
卷: 9, 期:2
语种: 英语
英文关键词: Imaging techniques ; Algorithms ; Remote sensing imagery ; Wildfires ; Remote sensing ; Curve fitting ; Forestry ; Forest ecology
英文摘要: 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.
URL: http://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0087480&type=printable
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/19936
Appears in Collections:过去全球变化的重建
影响、适应和脆弱性
科学计划与规划
气候变化与战略
全球变化的国际研究计划
气候减缓与适应
气候变化事实与影响

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作者单位: 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;Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijinng, China;Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijinng, China;Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijinng, China

Recommended Citation:
Yang Liu,Qin Dai,JianBo Liu,et al. Study of Burn Scar Extraction Automatically Based on Level Set Method using Remote Sensing Data[J]. PLOS ONE,2014-01-01,9(2)
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