The uncertainty of SAR platform position or non-ideal propagation mediums in synthetic aperture radar (SAR) imaging will cause phase error, which can result in image defocusing. To solve this problem, autofocusing technology in SAR imaging is used to correct the unknown phase error based on the collected data, and hence obtain the well focused image. In this paper, we propose an L1-norm regularization based sparse SAR autofocusing imaging method. The method uses an iterative algorithm to implement both phase error estimation and L1 regularization reconstruction. Firstly, the proposed method introduces the phase error term into SAR imaging observation model based on the autofocusing principle. Then it will estimate and correct phase error during the sparse imaging by solving an L1-norm regularization problem. And thus obtain well-focused sparse SAR images. Experimental results based on real data verify the effectiveness of the proposed method.
Synthetic aperture radar interferometry (InSAR) exploits two complex-valued images of the same target area to extract interferogram. However, due to the periodicity of trigonometric functions, the interferometric phase is only measured modulo 2π and wrapped between [-π,π). Therefore, phase unwrapping is necessary in InSAR processing for absolute terrain phase. The phase unwrapping method has a great influence on the accuracy of phase recovery. The phase difference between wrapped phase and unwrapped phase, usually called phase ambiguity cycle, can be estimated as an integer multiple of . The phase unwrapping problem is essentially the problem of estimating the phase ambiguity cycle. Since the phase ambiguity cycle is equal to zero in most cases, its estimation can be regarded as a sparse signal recovery problem. In this paper, an L1-norm regularization-based SAR phase unwrapping method is introduced to estimate the phase ambiguity cycle and obtain the high-quality absolute phase. In the proposed method, we firstly construct the phase unwrapping model based on the relationship between wrapped and absolute phase. Then the phase ambiguity cycle will be recovered by solving an L1-norm regularization problem. Experimental results based on simulated and real data validate the proposed method.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.