Share Email Print
cover

Journal of Applied Remote Sensing • Open Access

Multibaseline polarimetric synthetic aperture radar tomography of forested areas using wavelet-based distribution compressive sensing
Author(s): Lei Liang; Xinwu Li; Xizhang Gao; Huadong Guo

Paper Abstract

The three-dimensional (3-D) structure of forests, especially the vertical structure, is an important parameter of forest ecosystem modeling for monitoring ecological change. Synthetic aperture radar tomography (TomoSAR) provides scene reflectivity estimation of vegetation along elevation coordinates. Due to the advantages of super-resolution imaging and a small number of measurements, distribution compressive sensing (DCS) inversion techniques for polarimetric SAR tomography were successfully developed and applied. This paper addresses the 3-D imaging of forested areas based on the framework of DCS using fully polarimetric (FP) multibaseline SAR interferometric (MB-InSAR) tomography at the P-band. A new DCS-based FP TomoSAR method is proposed: a new wavelet-based distributed compressive sensing FP TomoSAR method (FP-WDCS TomoSAR method). The method takes advantage of the joint sparsity between polarimetric channel signals in the wavelet domain to jointly inverse the reflectivity profiles in each channel. The method not only allows high accuracy and super-resolution imaging with a low number of acquisitions, but can also obtain the polarization information of the vertical structure of forested areas. The effectiveness of the techniques for polarimetric SAR tomography is demonstrated using FP P-band airborne datasets acquired by the ONERA SETHI airborne system over a test site in Paracou, French Guiana.

Paper Details

Date Published: 23 October 2015
PDF: 12 pages
J. Appl. Remote Sens. 9(1) 095048 doi: 10.1117/1.JRS.9.095048
Published in: Journal of Applied Remote Sensing Volume 9, Issue 1
Show Author Affiliations
Lei Liang, Institute of Remote Sensing and Digital Earth (China)
Xinwu Li, Institute of Remote Sensing and Digital Earth (China)
Xizhang Gao, Institute of Geographic Sciences and Natural Resources Research (China)
Huadong Guo, Institute of Remote Sensing and Digital Earth (China)


© SPIE. Terms of Use
Back to Top