SAR image compression using optronic processing

Ma, Jiacheng; Yang, Baozhang; Gao, Yesheng; Leí, Tao; Liu, Xingzhao

doi:10.1049/joe.2019.0210

Cited by 3 publications

(1 citation statement)

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“…For example, a drone SAR system based on ground penetration radar enables safe detection of landmines [40,41], yet it requires efficient SAR data compression due to the limited storage and payload constraint [42]. The 2D DCT was employed for compression of space-born SAR images in [43]. To improve the compression efficiency, wavelet-based approaches were developed for efficient compression of SAR images by separately applying the DWT to the real and imaginary parts of complex SAR image data [44][45][46], and the compression performance related to the magnitude and phase was improved by employing the directional lifting wavelet transform (DLWT) [47].…”

Section: Introductionmentioning

confidence: 99%

Drone SAR Image Compression Based on Block Adaptive Compressive Sensing

Choi

Lee

2021

Remote Sensing

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In this paper, an adaptive block compressive sensing (BCS) method is proposed for compression of synthetic aperture radar (SAR) images. The proposed method enhances the compression efficiency by dividing the magnitude of the entire SAR image into multiple blocks and subsampling individual blocks with different compression ratios depending on the sparsity of coefficients in the discrete wavelet transform domain. Especially, a new algorithm is devised that selects the best block measurement matrix from a predetermined codebook to reduce the side information about measurement matrices transferred from the remote sensing node to the ground station. Through some modification of the iterative thresholding algorithm, a new clustered BCS recovery method is proposed that classifies the blocks into multiple clusters according to the compression ratio and iteratively reconstructs the SAR image from the received compressed data. Since the blocks in the same cluster are concurrently reconstructed using the same measurement matrix, the proposed structure mitigates the increase in computational complexity when adopting multiple measurement matrices. Using existing SAR images and experimental data obtained by self-made drone SAR and vehicular SAR systems, it is shown that the proposed scheme provides a good tradeoff between the peak signal-to-noise ratio and the computational load compared to conventional BCS-based compression techniques.

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