Abstract
Millimeter-wave interferometric synthetic aperture radiometer (InSAR) can provide high-resolution observations for many applications by using small antennas to achieve very large synthetic aperture. However, reconstruction of a millimeter-wave InSAR image has been proven to be an ill-posed inverse problem that degrades the performance of InSAR imaging. In this paper, a novel millimeter-wave InSAR image reconstruction approach, referred to as InSAR-TVMC, by total variation (TV) regularized matrix completion (MC) in two-dimensional data space, is proposed. Based on the a priori knowledge that natural millimeter-wave images statistically hold the low-rank property, the proposed approach represents the object images as low-rank matrices and formulates the data acquisition of InSAR in two-dimensional data space directly to undersample visibility function samples. Subsequently, using the undersampled visibility function samples, the optimal solution of the InSAR image reconstruction problem is obtained by simultaneously adopting MC techniques and TV regularization. Experimental results on simulated and real millimeter-wave InSAR image data demonstrate the effectiveness and the significant improvement of the reconstruction performance of the proposed InSAR-TVMC approach over conventional and one-dimensional sparse InSAR image reconstruction approaches.
Highlights
Millimeter-wave interferometric synthetic aperture radiometer (InSAR) is a powerful observation system with high-resolution for many applications across the geographical and life sciences, including remote sensing, atmosphere monitoring, weather and climate forecast, anti-terrorist and security check [1,2,3,4]
The millimeter-wave InSAR brightness temperature images are simulated for each baseline of InSAR according to Equation (1) to form the measured visibility function samples, which are used to reconstruct InSAR images via the Fourier transformation (FFT) approach, the compressive sensing (CS) approach and the InSAR-TVMC approach
After the comparison experiments on simulated millimeter-wave brightness temperature data of the Earth, we explored the InSAR-matrix completion (MC) approach on real millimeter-wave InSAR data, which were acquired by a Geostationary Interferometric Microwave Sounder (GIMS) demonstrator in the near field by the National Space Science Center of China [34]
Summary
Millimeter-wave interferometric synthetic aperture radiometer (InSAR) is a powerful observation system with high-resolution for many applications across the geographical and life sciences, including remote sensing, atmosphere monitoring, weather and climate forecast, anti-terrorist and security check [1,2,3,4]. The imaging principle of millimeter-wave InSAR is based on the Fourier transform between the acquired visibility function and modified millimeter-wave brightness temperature distribution of the imaged scene; FFT method is the basic approach to solve the millimeter-wave InSAR image reconstruction problem [1]. These difficulties in InSAR image reconstruction degrade performance and limit the application of InSAR in some scenarios
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