Special Section on Advances in Remote Sensing for Renewable Energy Development: Challenges and Perspectives

Forest biomass estimation using synthetic aperture radar polarimetric features

[+] Author Affiliations
Alireza Sharifi, Jalal Amini

University of Tehran, Department of Surveying and Geomatics Engineering, Tehran 11155-4563, Iran

J. Appl. Remote Sens. 9(1), 097695 (Jul 02, 2015). doi:10.1117/1.JRS.9.097695
History: Received February 25, 2015; Accepted June 4, 2015
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Abstract.  Polarimetric synthetic aperture radar (POLSAR) images have many applications in forest studies, especially for biomass estimation. An algorithm was proposed to extract optimized features from POLSAR images that are required for estimation. The algorithm included three main steps: feature extraction including radar backscatters and Pope’s, Cloude–Pottier’s, Freeman–Durden’s, and Touzi’s parameters; feature selection using a particle swarm optimization (PSO); and forest biomass estimation using multivariate relevance vector regression (MVRVR) and support vector regression. Based on the PSO, a combination of features was selected. The estimation based on the PSO selection was the most accurate, with the MVRVR model showing the highest coefficient of determination (R2, 0.86) and the lowest errors, with a root-mean square error of 39.17, a mean absolute error of 36.50, and a mean error of 11.59.

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© 2015 Society of Photo-Optical Instrumentation Engineers

Citation

Alireza Sharifi and Jalal Amini
"Forest biomass estimation using synthetic aperture radar polarimetric features", J. Appl. Remote Sens. 9(1), 097695 (Jul 02, 2015). ; http://dx.doi.org/10.1117/1.JRS.9.097695


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