Remote Sensing Applications and Decision Support

Enhanced detection of burned area using cross- and autocorrelation

[+] Author Affiliations
Tauqir Ahmed Moughal, Fusheng Yu

Beijing Normal University, Laboratory of Mathematics and Complex Systems, Ministry of Education, School of Mathematical Sciences, Beijing 100875, China

Abeer Mazher

Peking University, Institute of Remote Sensing and GIS, School of Earth and Space Sciences, Beijing 100871, China

Shihu Liu

Yunnan Minzu University, School of Mathematics and Computer Science, Kunming 650500, China

Abdul Razzaq

Beijing Normal University, Institute of Virtual Reality and Visualization Technology, Beijing 100875, China

J. Appl. Remote Sens. 9(1), 096018 (Aug 20, 2015). doi:10.1117/1.JRS.9.096018
History: Received February 22, 2015; Accepted July 22, 2015
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Abstract.  New and effective methods for burned area detection from remote sensing data are required by diverse applications. Burned area detection from a large heterogeneous landscape is a challenging task because of the low-interclass dissimilarity of burned and unburned pixels. Burned and unburned pixels may reflect similar spectral signatures but retain distinct temporal and spatial dependence in its vicinity. This change in temporal and spatial domains can be used to supplement spectral information. The existing methods use only spectral information of the images for detecting burned area and ignore the local temporal and spatial associations. We propose a new method to improve burned area detection accuracy by combining spectral information with local temporal and spatial associations. A method for simultaneous incorporation of these associations by means of cross- and autocorrelation is put forward and tested here under the logistic regression framework. The proposed method was assessed over two different study areas and showed significant increment of 5% to 9% in Kappa accuracy. We demonstrated that the use of cross- and autocorrelation leads to substantial increase in burned area detection accuracy.

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© 2015 Society of Photo-Optical Instrumentation Engineers

Citation

Tauqir Ahmed Moughal ; Fusheng Yu ; Abeer Mazher ; Shihu Liu and Abdul Razzaq
"Enhanced detection of burned area using cross- and autocorrelation", J. Appl. Remote Sens. 9(1), 096018 (Aug 20, 2015). ; http://dx.doi.org/10.1117/1.JRS.9.096018


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