SAR Images Compressed Sensing Based on Recovery Algorithms

Rouabah, Slim; Ouarzeddine, Mounira; Souissi, Boularbah

doi:10.1109/igarss.2018.8518037

Cited by 6 publications

(3 citation statements)

References 12 publications

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“…However, they cannot consider the under-sampled FMCW signals caused by the harsh environment inside the BF, which critically reduces the precision of burden surface profile imaging. The sparsity of FMCW signals makes the compressive sensing (CS)-based SAR imaging technique an alternative solution that has been extensively applied in various domains, such as in urban areas, forests, and oceans [18]. For inverse SAR (ISAR), a high-resolution gapped stepped-frequency waveform (GSFW) ISAR imaging framework was proposed in [19].…”

Section: Introductionmentioning

confidence: 99%

Increasing SAR Imaging Precision for Burden Surface Profile Jointly Using Low-Rank and Sparsity Priors

Ni,

Chen,

Hou

et al. 2024

Remote Sensing

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The synthetic aperture radar (SAR) imaging technique for a frequency-modulated continuous wave (FMCW) has attracted wide attention in the field of burden surface profile measurement. However, the imaging data are virtually under-sampled due to the severely restricted scan time, which prevents the antenna being exposed to high temperatures and heavy dust in the blast furnace (BF) for an extended period. In traditional SAR imaging algorithm research, the insufficient accumulation of scattered energy in reconstructing the burden surface profile leads to lower imaging precision, and the harsh smelting increases the probability of distortion in shape detection. In this study, to address these challenges, a novel rotating SAR imaging algorithm based on the constructed mechanical swing radar system is proposed. This algorithm is inspired by the low-rank property of the sampled signal matrix and the sparsity of burden surface profile images. First, the sparse FMCW signal is modeled, and the position transform matrix, calculated according to the BF dimensions, is embedded into the dictionary matrix. Then, the low-rank and sparsity priors are considered and reformulated as split variables in order to establish a convex optimization problem. Lastly, the augmented Lagrange multiplier (ALM) is employed to solve this problem under double constraints, and the imaging results are obtained using the alternating direction method of multipliers (ADMM). The experimental results demonstrate that, in the subsequent shape detection, the root mean square error (RMSE) is 15.38% lower than the previous algorithm and 15.63% lower under low signal-to-noise (SNR) conditions. In both enclosed and harsh environments, the proposed algorithm is able to achieve higher imaging precision even under high noise. It will be further optimized for speed and reliability, with plans to extend its application to 3D measurements in the future.

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Section: Introductionmentioning

confidence: 99%

Increasing SAR Imaging Precision for Burden Surface Profile Jointly Using Low-Rank and Sparsity Priors

Ni,

Chen,

Hou

et al. 2024

Remote Sensing

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“…The CS theory has been successfully addressed in raw data compression of synthetic aperture radar (SAR) [22][23][24], high-resolution SAR image formation [25][26][27][28][29][30][31], SAR imaging with motion compensation [32,33], and compression of SAR images [34,35]. Moreover, the CS-SAR imaging schemes have been verified through hardware implementations and field experiments [36][37][38][39].…”

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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“…Compressed sensing is a promising paradigm that uses signal sparsity to reduce the amount of data that needs to be measured [6,7]. Compressed sensing (CS) theory indicates that the characteristics of sparse signals are not affected by the signal sparse basis.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Traffic Prediction with Adaptive Sampling for 5G HetNet IoT Applications

Mao

Liu

et al. 2019

Wireless Communications and Mobile Computing

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Due to the proliferation of global monitoring sensors, the Internet of Things (IoT) is widely used to build smart cities and smart homes. 5G HetNets play an important role in the IoT video stream. This paper proposes an improved Call Session Control Function (CSCF) scheme. The improved CSCF server contains additional modules to facilitate IoT traffic prediction and resource reservation. We highlight traffic prediction in this work and develop a compressed sensing based linear predictor to catch the traffic patterns. Experimental results justify that our proposed scheme can forecast the traffic load with high accuracy but low sampling overhead.

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SAR Images Compressed Sensing Based on Recovery Algorithms

Cited by 6 publications

References 12 publications

Increasing SAR Imaging Precision for Burden Surface Profile Jointly Using Low-Rank and Sparsity Priors

Increasing SAR Imaging Precision for Burden Surface Profile Jointly Using Low-Rank and Sparsity Priors

Drone SAR Image Compression Based on Block Adaptive Compressive Sensing

Dynamic Traffic Prediction with Adaptive Sampling for 5G HetNet IoT Applications

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