Drone SAR Image Compression Based on Block Adaptive Compressive Sensing
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 wit...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-10-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/19/3947 |
| _version_ | 1827680592914808832 |
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| author | Jihoon Choi Wookyung Lee |
| author_facet | Jihoon Choi Wookyung Lee |
| author_sort | Jihoon Choi |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T06:51:52Z |
| format | Article |
| id | doaj.art-27fa8f62eff64728999d68b4d07b6c80 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T06:51:52Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-27fa8f62eff64728999d68b4d07b6c802023-11-22T16:43:12ZengMDPI AGRemote Sensing2072-42922021-10-011319394710.3390/rs13193947Drone SAR Image Compression Based on Block Adaptive Compressive SensingJihoon Choi0Wookyung Lee1School of Electronics and Information Engineering, Korea Aerospace University, Goyang-Si 10540, Gyeonggi-do, KoreaSchool of Electronics and Information Engineering, Korea Aerospace University, Goyang-Si 10540, Gyeonggi-do, KoreaIn 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.https://www.mdpi.com/2072-4292/13/19/3947block compressive sensingsynthetic aperture radaradaptive measurement ratiodual-tree discrete wavelet transform |
| spellingShingle | Jihoon Choi Wookyung Lee Drone SAR Image Compression Based on Block Adaptive Compressive Sensing Remote Sensing block compressive sensing synthetic aperture radar adaptive measurement ratio dual-tree discrete wavelet transform |
| title | Drone SAR Image Compression Based on Block Adaptive Compressive Sensing |
| title_full | Drone SAR Image Compression Based on Block Adaptive Compressive Sensing |
| title_fullStr | Drone SAR Image Compression Based on Block Adaptive Compressive Sensing |
| title_full_unstemmed | Drone SAR Image Compression Based on Block Adaptive Compressive Sensing |
| title_short | Drone SAR Image Compression Based on Block Adaptive Compressive Sensing |
| title_sort | drone sar image compression based on block adaptive compressive sensing |
| topic | block compressive sensing synthetic aperture radar adaptive measurement ratio dual-tree discrete wavelet transform |
| url | https://www.mdpi.com/2072-4292/13/19/3947 |
| work_keys_str_mv | AT jihoonchoi dronesarimagecompressionbasedonblockadaptivecompressivesensing AT wookyunglee dronesarimagecompressionbasedonblockadaptivecompressivesensing |