Multichannel synthetic aperture radar (MC-SAR) allows for high-resolution imaging of a wide swath (HRWS), at the cost of acquiring and downlinking a significantly larger amount of data, compared with conventional SAR ...
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Multichannel synthetic aperture radar (MC-SAR) allows for high-resolution imaging of a wide swath (HRWS), at the cost of acquiring and downlinking a significantly larger amount of data, compared with conventional SAR systems. In this letter, we discuss the potential of efficient data volume reduction (DVR) for MC-SAR. Specifically, we focus on methods based on transform coding (TC) and linear predictive coding (LPC), which exploit the redundancy introduced in the rawdata by the finer azimuth sampling peculiar to the MC system. The proposed approaches, in combination with a variable-bit quantization, allow for the optimization of the resulting performance and data rate. We consider three exemplary yet realistic MC-SAR systems, and we conduct simulations and analyses on synthetic SAR data considering different radar backscatter distributions, which demonstrate the effectiveness of the proposed methods.
Present and next-generation synthetic aperture radar (SAR) missions require an increasing volume of onboard data, due to the employment of large bandwidths, multiple channels and polarizations, and large swath widths ...
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ISBN:
(纸本)9798350360332;9798350360325
Present and next-generation synthetic aperture radar (SAR) missions require an increasing volume of onboard data, due to the employment of large bandwidths, multiple channels and polarizations, and large swath widths acquired by bi- and multi-static sensor configurations. This leads to stringent requirements in terms of onboard memory and downlink capacity, hence making the proper quantization of the SAR rawdata represents an task of utmost importance, as it affects the amount of data but also the quality of the SAR and InSAR products. This paper presents novel methods for efficient SAR rawdata compression, which make use of artificial intelligence for the joint optimization of bitrate allocation and the resulting performance and exploit the potential of transform and predictive coding schemes for data volume reduction in the context of multi-azimuth channel (MAC) SAR. Simulations and analyses on real data are presented, showing the suitability of the proposed methods.
Present and future spaceborne synthetic aperture radar (SAR) missions are designed to acquire an increasingly large amount of onboard data. This is a consequence of the use of large bandwidths, multiple polarizations,...
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Present and future spaceborne synthetic aperture radar (SAR) missions are designed to acquire an increasingly large amount of onboard data. This is a consequence of the use of large bandwidths, multiple polarizations, and the acquisition of large swath widths at fine spatial resolutions, which result in challenging requirements in terms of onboard memory and downlink capacity. In this scenario, SAR raw data quantization represents an essential aspect, as it affects the volume of data to be stored and transmitted to the ground as well as the quality of the resulting SAR products. Dynamic predictive block-adaptive quantization (DP-BAQ) is a novel technique, recently proposed by the authors, consisting of a low-complexity data compression method, and its application is particularly suitable for staggered SAR systems. DP-BAQ exploits the existing correlation among the azimuth rawdata samples by applying linear predictive coding (LPC). This results in a data rate reduction of up to 25% with respect to state-of-the-art SAR quantization methods. In this letter, we test and validate the potential of DP-BAQ on airborne SAR data which emulates the system scenario of Tandem-L, a German Aerospace Center (DLR) mission proposal for a bistatic L-band system. For this purpose, an experimental SAR image has been acquired at the L-band by the airborne DLR flugzeug-SAR (F-SAR) sensor over the Kaufbeuren area, in Southern Germany. In order to simulate the staggered SAR acquisition mode, we implemented a dedicated resampling and filtering of the data. Our analyses confirm the effectiveness of DP-BAQ for efficient data volume reduction, exhibiting a consistent and promising performance when tested on areas characterized by different land cover types and backscatter statistics.
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