Superpixel based recursive least-squares method for lossless compression of hyperspectral images

Karaca, Ali Can; Güllü, M. Kemal

doi:10.1007/s11045-018-0590-4

Cited by 14 publications

(7 citation statements)

References 28 publications

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“…We note that complex image preprocessing can achieve better compression results, such as band reorder, clustering, and super-pixel methods [30,31]. However, this is beyond the scope of this paper and will not be discussed here.…”

Section: Compression Resultsmentioning

confidence: 98%

Recursive Least Squares for Near-Lossless Hyperspectral Data Compression

et al. 2022

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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.

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Section: Compression Resultsmentioning

confidence: 98%

Recursive Least Squares for Near-Lossless Hyperspectral Data Compression

et al. 2022

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“…Due to the improved band prediction, the method achieves a comparable lossless compression performance, when compared to adaptive-length and fixed-length CRLS while providing relatively lower computational times. Additionally, two parallel methods, SuperRLS and BSuperRLS, are proposed for lossless compression of hyperspectral images in [61]. These methods involve superpixel segmentation, parallel prediction using Recursive Least-squares, and encoding residuals with an arithmetic encoder.…”

Section: Related Workmentioning

confidence: 99%

Lossless and Near-Lossless Compression Algorithms for Remotely Sensed Hyperspectral Images

Altamimi,

Ben Youssef

2024

Entropy

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Rapid and continuous advancements in remote sensing technology have resulted in finer resolutions and higher acquisition rates of hyperspectral images (HSIs). These developments have triggered a need for new processing techniques brought about by the confined power and constrained hardware resources aboard satellites. This article proposes two novel lossless and near-lossless compression methods, employing our recent seed generation and quadrature-based square rooting algorithms, respectively. The main advantage of the former method lies in its acceptable complexity utilizing simple arithmetic operations, making it suitable for real-time onboard compression. In addition, this near-lossless compressor could be incorporated for hard-to-compress images offering a stabilized reduction at nearly 40% with a maximum relative error of 0.33 and a maximum absolute error of 30. Our results also show that a lossless compression performance, in terms of compression ratio, of up to 2.6 is achieved when testing with hyperspectral images from the Corpus dataset. Further, an improvement in the compression rate over the state-of-the-art k2-raster technique is realized for most of these HSIs by all four variations of our proposed lossless compression method. In particular, a data reduction enhancement of up to 29.89% is realized when comparing their respective geometric mean values.

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“…Prediction-based algorithm is also used in conjunction with other algorithms to improve performance. Some state-of-the-art algorithms are 3D-DPCM, 33 superpixel-based segmentation-CRLS, 34 RLS-adaptice length prediction, 35 and LMS-APL. 36 Technique.…”

Section: Prediction Algorithmsmentioning

confidence: 99%

“…The results of each have been compared with existing algorithms and suggestions are provided. Super RLS 34 method includes some steps such as intraband encoding, superpixel segmentation, vectorization, RLS prediction, and entropy encoding. In the first step, spatial correlation is removed from the input image by subtracting the arithmetic mean of neighborhood pixels from each pixel in each band.…”

Section: Cang and Wang 51mentioning

confidence: 99%

Comprehensive review of hyperspectral image compression algorithms

2020

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Rapid advancement in the development of hyperspectral image analysis techniques has led to specialized hyperspectral missions. It results in the bulk transmission of hyperspectral images from sensors to analysis centers and finally to data centers. Storage of these large size images is a critical issue that is handled by compression techniques. This survey focuses on different hyperspectral image compression algorithms that have been classified into two broad categories based on eight internal and six external parameters. In addition, we identified research challenges and suggested future scope for each technique. The detailed classification used in this paper can categorize other compression algorithms and may help in selecting research objectives.

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Superpixel based recursive least-squares method for lossless compression of hyperspectral images

Cited by 14 publications

References 28 publications

Recursive Least Squares for Near-Lossless Hyperspectral Data Compression

Recursive Least Squares for Near-Lossless Hyperspectral Data Compression

Lossless and Near-Lossless Compression Algorithms for Remotely Sensed Hyperspectral Images

Comprehensive review of hyperspectral image compression algorithms

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