High-Performance Lossless Compression of Hyperspectral Remote Sensing Scenes Based on Spectral Decorrelation

Hernández-Cabronero, Miguel; Portell, J.; Blanes, Ian; Serra-Sagristà, Joan

doi:10.3390/rs12182955

Cited by 14 publications

(8 citation statements)

References 32 publications

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“…FAPEC is equipped with various pre-processing stages, including a basic differential coder, multi-band prediction and a wavelet transform [ 10 ]. In its latest version, FAPEC 22.0, an algorithm for water column and bathymetry data, was also added [ 11 , 12 ].…”

Section: The Fapec Data Compressormentioning

confidence: 99%

Context-Aware Lossless and Lossy Compression of Radio Frequency Signals

Martí

Portell

Riba

et al. 2023

Sensors

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We propose an algorithm based on linear prediction that can perform both the lossless and near-lossless compression of RF signals. The proposed algorithm is coupled with two signal detection methods to determine the presence of relevant signals and apply varying levels of loss as needed. The first method uses spectrum sensing techniques, while the second one takes advantage of the error computed in each iteration of the Levinson–Durbin algorithm. These algorithms have been integrated as a new pre-processing stage into FAPEC, a data compressor first designed for space missions. We test the lossless algorithm using two different datasets. The first one was obtained from OPS-SAT, an ESA CubeSat, while the second one was obtained using a SDRplay RSPdx in Barcelona, Spain. The results show that our approach achieves compression ratios that are 23% better than gzip (on average) and very similar to those of FLAC, but at higher speeds. We also assess the performance of our signal detectors using the second dataset. We show that high ratios can be achieved thanks to the lossy compression of the segments without any relevant signal.

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Section: The Fapec Data Compressormentioning

confidence: 99%

Context-Aware Lossless and Lossy Compression of Radio Frequency Signals

Martí

Portell

Riba

et al. 2023

Sensors

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“…Hernández-Cabronero et al 89 aimed to determine the most beneficial trade-off of compression-complexity inside the state-of-the-art in HSI compression. For performance assessment, the authors combined the adequately reflect the non-linear feature of a hyperspectral image.…”

Section: Wavelet Transforms For Image and Spectra Compressionmentioning

confidence: 99%

An outlook: machine learning in hyperspectral image classification and dimensionality reduction techniques

Reddy

Harikiran

2022

J. Spectral Imaging

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Hyperspectral imaging is used in a wide range of applications. When used in remote sensing, satellites and aircraft are employed to collect the images, which are used in agriculture, environmental monitoring, urban planning and defence. The exact classification of ground features in the images is a significant research issue and is currently receiving greater attention. Moreover, these images have a large spectral dimensionality, which adds computational complexity and affects classification precision. To handle these issues, dimensionality reduction is an essential step that improves the performance of classifiers. In the classification process, several strategies have produced good classification results. Of these, machine learning techniques are the most powerful approaches. As a result, this paper reviews three different types of hyperspectral image machine learning classification methods: cluster analysis, supervised and semi-supervised classification. Moreover, this paper shows the effectiveness of all these techniques for hyperspectral image classification and dimensionality reduction. Furthermore, this review will assist as a reference for future research to improve the classification and dimensionality reduction approaches.

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“…The The paper "High-Performance Lossless Compression of Hyperspectral Remote Sensing Scenes Based on Spectral Decorrelation" by Hernández-Cabronero, M., Portell, J., Blanes, I., Serra-Sagristà, J. [5] investigates the most advantageous compression-complexity trade-off in hyperspectral image (HSI) compression. Compression performance and execution time results are obtained for a set of 47 HSI scenes produced by 14 different sensors in real remote sensing missions.…”

Section: Overview Of the Issue: Remote Sensing Data Compressionmentioning

confidence: 99%

Editorial to Special Issue “Remote Sensing Data Compression”

Vozel

Serra-Sagristà

2021

Remote Sensing

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A huge amount of remote sensing data is acquired each day, which is transferred to image processing centers and/or to customers. Due to different limitations, compression has to be applied on-board and/or on-the-ground. This Special Issue collects 15 papers dealing with remote sensing data compression, introducing solutions for both lossless and lossy compression, analyzing the impact of compression on different processes, investigating the suitability of neural networks for compression, and researching on low complexity hardware and software approaches to deliver competitive coding performance.

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High-Performance Lossless Compression of Hyperspectral Remote Sensing Scenes Based on Spectral Decorrelation

Cited by 14 publications

References 32 publications

Context-Aware Lossless and Lossy Compression of Radio Frequency Signals

Context-Aware Lossless and Lossy Compression of Radio Frequency Signals

An outlook: machine learning in hyperspectral image classification and dimensionality reduction techniques

Editorial to Special Issue “Remote Sensing Data Compression”

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