Recursive Least Squares for Near-Lossless Hyperspectral Data Compression
The hyperspectral image compression scheme is a trade-off between the limited hardware resources of the on-board platform and the ever-growing resolution of the optical instruments. Predictive coding attracts researchers due to its low computational complexity and moderate memory requirements. We pr...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/14/7172 |
| _version_ | 1827598105665601536 |
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| author | Tie Zheng Yuqi Dai Changbin Xue Li Zhou |
| author_facet | Tie Zheng Yuqi Dai Changbin Xue Li Zhou |
| author_sort | Tie Zheng |
| collection | DOAJ |
| description | The hyperspectral image compression scheme is a trade-off between the limited hardware resources of the on-board platform and the ever-growing resolution of the optical instruments. Predictive coding attracts researchers due to its low computational complexity and moderate memory requirements. We propose a near-lossless prediction-based compression scheme that removes spatial and spectral redundant information, thereby significantly reducing the size of hyperspectral images. This scheme predicts the target pixel’s value via a linear combination of previous pixels. The weight matrix of the predictor is iteratively updated using a recursive least squares filter with a loop quantizer. The optimal number of bands for prediction was analyzed experimentally. The results indicate that the proposed scheme outperforms state-of-the-art compression methods in terms of the compression ratio and quality retrieval. |
| first_indexed | 2024-03-09T03:43:47Z |
| format | Article |
| id | doaj.art-bda2ed1e7e1643c7908d912048d5a6fa |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T03:43:47Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-bda2ed1e7e1643c7908d912048d5a6fa2023-12-03T14:36:45ZengMDPI AGApplied Sciences2076-34172022-07-011214717210.3390/app12147172Recursive Least Squares for Near-Lossless Hyperspectral Data CompressionTie Zheng0Yuqi Dai1Changbin Xue2Li Zhou3National Space Science Center, Chinese Academy of Sciences, Beijing 100190, ChinaNational Space Science Center, Chinese Academy of Sciences, Beijing 100190, ChinaNational Space Science Center, Chinese Academy of Sciences, Beijing 100190, ChinaNational Space Science Center, Chinese Academy of Sciences, Beijing 100190, ChinaThe hyperspectral image compression scheme is a trade-off between the limited hardware resources of the on-board platform and the ever-growing resolution of the optical instruments. Predictive coding attracts researchers due to its low computational complexity and moderate memory requirements. We propose a near-lossless prediction-based compression scheme that removes spatial and spectral redundant information, thereby significantly reducing the size of hyperspectral images. This scheme predicts the target pixel’s value via a linear combination of previous pixels. The weight matrix of the predictor is iteratively updated using a recursive least squares filter with a loop quantizer. The optimal number of bands for prediction was analyzed experimentally. The results indicate that the proposed scheme outperforms state-of-the-art compression methods in terms of the compression ratio and quality retrieval.https://www.mdpi.com/2076-3417/12/14/7172near-lossless compressionrecursive least squareshyperspectral imagepredictive coding |
| spellingShingle | Tie Zheng Yuqi Dai Changbin Xue Li Zhou Recursive Least Squares for Near-Lossless Hyperspectral Data Compression Applied Sciences near-lossless compression recursive least squares hyperspectral image predictive coding |
| title | Recursive Least Squares for Near-Lossless Hyperspectral Data Compression |
| title_full | Recursive Least Squares for Near-Lossless Hyperspectral Data Compression |
| title_fullStr | Recursive Least Squares for Near-Lossless Hyperspectral Data Compression |
| title_full_unstemmed | Recursive Least Squares for Near-Lossless Hyperspectral Data Compression |
| title_short | Recursive Least Squares for Near-Lossless Hyperspectral Data Compression |
| title_sort | recursive least squares for near lossless hyperspectral data compression |
| topic | near-lossless compression recursive least squares hyperspectral image predictive coding |
| url | https://www.mdpi.com/2076-3417/12/14/7172 |
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