Spectral Distortion in Lossy Compression of Hyperspectral Data

Aiazzi, Bruno; Alparone, Luciano; Baronti, Stefano; Lastri, Cinzia; Selva, Massimo

doi:10.1155/2012/850637

Cited by 29 publications

(14 citation statements)

References 23 publications

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“…Evidently, a lossy algorithm can reach higher CRs, but with the disadvantage of several information losses. The issue is then if a minimization of such a distortion is possible, to achieve decoded data with a sufficient quality [26]. However, in several applications, a lossless compression is mandatory.…”

Section: On-board Data Compressionmentioning

confidence: 99%

Influence of the System MTF on the On-Board Lossless Compression of Hyperspectral Raw Data

et al. 2019

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A noticeable topic to be pursued in the field of on-board real-time data processing is the influence of the modulation transfer function (MTF) of the image acquisition system on the lossless compressibility of raw (that is, uncalibrated) hyperspectral data. Actually, notwithstanding the system device is constrained by several design and manufacturing requirements, the impact of the on-board MTF on the performance of data compressors is becoming remarkable. In particular, the aim of reducing both transmission bandwidth/power and mass storage can be efficiently pursued. Such an analysis is expected to be useful especially for systems employed in mini-satellites, whose payload must be compact and light. From this perspective, this paper investigates the performance of a typical imaging system that acquires low/medium-spatial-resolution images, by considering high-resolution reference data, which simulate the real scene to be imaged. To this end, standard Consultative Committee for Space Data Systems (CCSDS) Aviris 2006 data have been chosen, due to their spatial resolution of 17 m, which is adequate to be a reference for simulated data whose spatial resolution is foreseen between 50 and 150 m. MTF requirements are usually provided based on the cut-off value of the amplitude at the Nyquist frequency, which is defined as a half of the sampling frequency. Typically, a cut-off value between 0 . 2 and 0 . 3 ensures that a sufficient amount of information is delivered from the scene to the acquired image, by avoiding at the same time the degradation due to an excessive aliasing distortion. All the scores are achieved by running the standard lossless compression scheme CCSDS 1.2.3.0-B-1 for multispectral/hyperspectral data, as a function of the cut-off value and different noise acquisition levels. The final results, and related plots, show that this analysis can suggest a suitable choice for the cut-off value, to ensure both a sufficient quality and low bit rates for the transmitted data to the ground station.

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Section: On-board Data Compressionmentioning

confidence: 99%

Influence of the System MTF on the On-Board Lossless Compression of Hyperspectral Raw Data

et al. 2019

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“…Such information is the same as noise to any statistical model, even if it is unique and accurate. Thus, processing a large number of hyperspectral bands can result in higher classification inaccuracy than processing a subset of relevant bands without redundancy (Zhao et al, 2011;Darvishzadeha et al, 2011;Amro et al, 2011;Aiazzi et al, 2012). Figure 10 and 11 show RMSE and RASE for the damaged side of the wishbone over three bands.…”

Section: Ajasmentioning

confidence: 99%

A New Approach to Damage Detection in Composite Body Structures Using Time Compensated Hyperspectral Image Analysis Technique

Iskandarani

2013

American Journal of Applied Sciences

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A novel approach to damage detection in composite car body structures using hyperspectral image index analysis algorithm is presented and discussed. The adapted technique allows the monitoring and analysis of a components structure based on correlation between time spaced thermal images. The technique produces several organized tables resulting from image fusion and frame deviation pixel redistribution calculations, which results in computable matrices. The obtained results proved the technique to be capable of classifying damage with ability to model various types of damage under various conditions. There is a need to accurately identify damaged composite components. This will enable the used algorithm to determine if the level of damage or defect in the component is critical according to established database that takes into account mechanical and physical factors. Testing different composite structures using Hyperspectral technique and Pulse Video Thermography (PVT), as hyperspectral uses different bands with different wavelengths to analyze the image results, which enables better recognition and classification. Successful recognition and component classification obtained with characterization of tested composite

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“…At this step, it may be useful to investigate the effects of a lossy compression of radiance data in terms of changes in spectral angle with respect to reflectance spectra obtained from uncompressed radiance data. 23,24 A more general approach that is pursued in this work is investigating the loss in spectral information due to an irreversible compression. 25 Such a study is complicated by the fact that the available data are a noisy realization of an unavailable ideal spectral information source that is assumed to be noise-free.…”

Section: Hyperspectral Remote Sensing From Satellitementioning

confidence: 99%

Information-theoretic assessment of on-board near-lossless compression of hyperspectral data

Aiazzi

Alparone

Baronti

et al. 2013

J. Appl. Remote Sens

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Abstract.A rate-distortion model to measure the impact of near-lossless compression of raw data, that is, compression with user-defined maximum absolute error, on the information available once the compressed data have been received and decompressed is proposed. Such a model requires the original uncompressed raw data and their measured noise variances. Advanced nearlossless methods are exploited only to measure the entropy of the datasets but are not required for on-board compression. In substance, the acquired raw data are regarded as a noisy realization of a noise-free spectral information source. The useful spectral information at the decoder is the mutual information between the unknown ideal source and the decoded source, which is affected by both instrument noise and compression-induced distortion. Experiments on simulated noisy images, in which the noise-free source and the noise realization are exactly known, show the trend of spectral information versus compression distortion, which in turn is related to the coded bit rate or equivalently to the compression ratio through the rate-distortion characteristic of the encoder used on satellite. Preliminary experiments on airborne visible infrared imaging spectrometer (AVIRIS) 2006 Yellowstone sequences match the trends of the simulations. The main conclusion that can be drawn is that the noisier the dataset, the lower the CR that can be tolerated, in order to save a prefixed amount of spectral information. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Spectral Distortion in Lossy Compression of Hyperspectral Data

Cited by 29 publications

References 23 publications

Influence of the System MTF on the On-Board Lossless Compression of Hyperspectral Raw Data

Influence of the System MTF on the On-Board Lossless Compression of Hyperspectral Raw Data

A New Approach to Damage Detection in Composite Body Structures Using Time Compensated Hyperspectral Image Analysis Technique

Information-theoretic assessment of on-board near-lossless compression of hyperspectral data

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