Joint Regularization of Phase and Amplitude of InSAR Data: Application to 3-D Reconstruction

被引：40

作者：

Denis, Loic ^{[2
]}

Tupin, Florence ^{[1
]}

Darbon, Jerome ^{[3
]}

Sigelle, Marc ^{[1
]}

机构：

[1] TELECOM ParisTech, Inst TELECOM, CNRS LTCI, F-75634 Paris, France

[2] CNRS, Lab Hubert Curien, F-42000 St Etienne, France

[3] Univ Calif Los Angeles, Dept Math, Los Angeles, CA 90095 USA

来源：

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING | 2009年 / 47卷 / 11期

基金：

美国国家科学基金会;

关键词：

Denoising; Markov random field (MRF); minimization methods; speckle; synthetic aperture radar (SAR); total variation (TV); CONSTRAINED TOTAL VARIATION; MARKOV RANDOM-FIELD; IMAGE-RESTORATION; ENERGY MINIMIZATION; GRAPH; ALGORITHM; SAR; SEGMENTATION; OPTIMIZATION; EXTRACTION;

D O I：

10.1109/TGRS.2009.2023668

中图分类号：

P3 [地球物理学]; P59 [地球化学];

学科分类号：

0708 ; 070902 ;

摘要：

Interferometric synthetic aperture radar (SAR) images suffer from a strong noise, and their regularization is often a prerequisite for successful use of their information. Independently of the unwrapping problem, interferometric phase denoising is a difficult task due to shadows and discontinuities. In this paper, we propose to jointly filter phase and amplitude data in a Markovian framework. The regularization term is expressed by the minimization of the total variation and may combine different information (phase, amplitude, optical data). First, a fast and approximate optimization algorithm for vectorial data is briefly presented. Then, two applications are described. The first one is a direct application of this algorithm for 3-D reconstruction in urban areas with very high resolution images. The second one is an adaptation of this framework to the fusion of SAR and optical data. Results on aerial SAR images are presented.

引用

页码：3774 / 3785

页数：12

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