A Fast and Accurate Basis Pursuit Denoising Algorithm With Application to Super-Resolving Tomographic SAR

Yilei Shi, Xiao Xiang Zhu, Wotao Yin, and Richard Bamler

Published in IEEE T. Geoscience and Remote Sensing:


This paper introduces a denoising algorithm based on basis pursuit (or l1 minimization) for super-resolving TomoSAR or tomographic synthetic aperture radar. The following image illustrates the geometry of TomoSAR.

TomoSAR imaging geometry illustration 
TomoSAR imaging geometry illustration

Among many applications of TomoSAR is to measure the elavation of objects and detect their motions and deformation. The algorithm proposed in this paper is capable of achieving these goals by processing lots of data in a short amount of time.


In the above figure, (a) is optical image from Google Maps; (b) is linear deformation caused by the construction of new buildings; (c) is the time lapse of optical images from Google Earth. Red is movement toward the sensor, and blue is away from the sensor.


Y. Shi, X.X. Zhu, W. Yin, and R. Bamler, A fast and accurate basis pursuit denoising algorithm with application to super-resolving tomographic SAR IEEE T. Geoscience and Remote Sensing 56(10), 6148-6158, 2018.

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