Regularization—The Compressive Sensing Approach
نویسنده
چکیده
Synthetic aperture radar (SAR) tomography (TomoSAR) extends the synthetic aperture principle into the elevation direction for 3-D imaging. The resolution in the elevation direction depends on the size of the elevation aperture, i.e., on the spread of orbit tracks. Since the orbits of modern meterresolution spaceborne SAR systems, like TerraSAR-X, are tightly controlled, the tomographic elevation resolution is at least an order of magnitude lower than in range and azimuth. Hence, super-resolution reconstruction algorithms are desired. The high anisotropy of the 3-D tomographic resolution element renders the signals sparse in the elevation direction; only a few pointlike reflections are expected per azimuth–range cell. This property suggests using compressive sensing (CS) methods for tomographic reconstruction. This paper presents the theory of 4-D (differential, i.e., space–time) CS TomoSAR and compares it with parametric (nonlinear least squares) and nonparametric (singular value decomposition) reconstruction methods. Super-resolution properties and point localization accuracies are demonstrated using simulations and real data. A CS reconstruction of a building complex from TerraSAR-X spotlight data is presented.
منابع مشابه
An Efficient Algorithm For Total Variation Regularization with Applications to the Single Pixel Camera and Compressive Sensing
An Efficient Algorithm For Total Variation Regularization with Applications to the Single Pixel Camera and Compressive Sensing
متن کاملAn Efficient Algorithm For Total Variation Regularization with Applications to the Single Pixel Camera and Compressive Sensing by Chengbo Li A
An Efficient Algorithm For Total Variation Regularization with Applications to the Single Pixel Camera and Compressive Sensing
متن کاملRecovery of Seismic Wavefields Based on Compressive Sensing by an l1-Norm Constrained Trust Region Method and the Piecewise Random Sub-sampling
SUMMARY Due to the influence of variations in landform, geophysical data acquisition is usually sub-sampled. Reconstruction of the seismic wavefield from sub-sampled data is an ill-posed inverse problem. Compressive sensing can be used to recover the original geophysical data from the sub-sampled data. In this paper, we consider the wavefield reconstruction problem as a com-pressive sensing and...
متن کاملSTCS-GAF: Spatio-Temporal Compressive Sensing in Wireless Sensor Networks- A GAF-Based Approach
Routing and data aggregation are two important techniques for reducing communication cost of wireless sensor networks (WSNs). To minimize communication cost, routing methods can be merged with data aggregation techniques. Compressive sensing (CS) is one of the effective techniques for aggregating network data, which can reduce the cost of communication by reducing the amount of routed data to t...
متن کاملA Simple Homotopy Proximal Mapping Algorithm for Compressive Sensing
In this paper, we present a novel yet simple homotopy proximal mapping algorithm for compressive sensing. The algorithm adopts a simple proximal mapping for l1 norm regularization at each iteration and gradually reduces the regularization parameter of the l1 norm. We prove a global linear convergence for the proposed homotopy proximal mapping (HPM) algorithm for solving compressive sensing unde...
متن کاملCompressive Sensing for Pan-sharpening
Based on compressive sensing framework and sparse reconstruction technology, a new pan-sharpening method, named Sparse Fusion of Images (SparseFI, pronounced as sparsify), is proposed in [1]. In this paper, the proposed SparseFI algorithm is validated using UltraCam and WorldView-2 data. Visual and statistic analysis show superior performance of SparseFI compared to the existing conventional pa...
متن کامل