Enhanced detection of burned area using cross- and autocorrelation

Tauqir Ahmed Moughal; Fusheng Yu; Abeer Mazher; Shihu Liu; Abdul Razzaq

doi:10.1117/1.JRS.9.096018

20 August 2015 Enhanced detection of burned area using cross- and autocorrelation

Tauqir Ahmed Moughal, Fusheng Yu, Abeer Mazher, Shihu Liu, Abdul Razzaq

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 9, Issue 1, 096018 (August 2015). https://doi.org/10.1117/1.JRS.9.096018

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.

Citation Download Citation

Tauqir Ahmed Moughal, Fusheng Yu, Abeer Mazher, Shihu Liu, and Abdul Razzaq "Enhanced detection of burned area using cross- and autocorrelation," Journal of Applied Remote Sensing 9(1), 096018 (20 August 2015). https://doi.org/10.1117/1.JRS.9.096018

Published: 20 August 2015

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available