  To avoid decorrelation, conventional synthetic aperture radar interferometry
(InSAR) requires that interferometric images should have a common spectral band
and the same resolution after proper preprocessing. For a high-resolution (HR)
image and a low-resolution (LR) one, the interferogram quality is limited by
the LR one since the non-common band (NCB) between two images is usually
discarded. In this article, we try to establish an InSAR method to improve
interferogram quality by means of exploiting the NCB. To this end, we first
define a new interferogram, which has the same resolution as the HR image. Then
we formulate the interferometric relationship between the two images into a
compressive sensing (CS) model, which contains the proposed HR interferogram.
With the sparsity of interferogram in appropriate domains, we model the
interferogram formation as a typical sparse recovery problem. Due to the
speckle effect in coherent radar imaging, the sensing matrix of our CS model is
inherently random. We theoretically prove that the sensing matrix satisfies
restricted isometry property, and thus the interferogram recovery performance
is guaranteed. Furthermore, we provide a fast interferogram formation algorithm
by exploiting computationally efficient structures of the sensing matrix.
Numerical experiments show that the proposed method provides better
interferogram quality in the sense of reduced phase noise and obtain
extrapolated interferogram spectra with respect to CB processing.

Signal Processing