The PRISMA payload optomechanical design, a high performance instrument for a new hyperspectral mission

Labate, Demetrio; Ceccherini, Massimo; Cisbani, A.; Cosmo, V. De; Galeazzi, Claudio; Giunti, Lorenzo; Melozzi, Mauro; Pieraccini, Stefano; Stagi, M.

doi:10.1016/j.actaastro.2009.03.077

Cited by 79 publications

(43 citation statements)

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“…Time-series spectral measurements of large areas are also very important. Future IS from orbit will enable global coverage of every point on the globe with very good signal-to-noise ratio, such as the PRISMA (Labate et al 2009) ,HyspIRI (Knox et al 2010) and EnMAP (Stuffler et al 2007) programs. This will enable monitoring soil surface changes in short-and long-term scenarios.…”

Section: Future Potential Of Remote Sensing Technology For Assessing mentioning

confidence: 99%

Reflectance Spectroscopy as a Tool for Monitoring Contaminated Soils

Schwartz¹,

Eshel²,

Ben‐Dor³

2011

Soil Contamination

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Section: Future Potential Of Remote Sensing Technology For Assessing mentioning

confidence: 99%

Reflectance Spectroscopy as a Tool for Monitoring Contaminated Soils

Schwartz¹,

Eshel²,

Ben‐Dor³

2011

Soil Contamination

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“…Earth observing (EO) satellites, endowed with a high temporal resolution, enable the retrieval and hence monitoring of plant biophysical parameters [1][2][3]. With the forthcoming super-spectral Copernicus' Sentinel-2 [4] and Sentinel-3 missions [5] as well as the planned EnMAP [6], HyspIRI [7] and PRISMA [8] imaging spectrometer missions, an unprecedented data stream becomes available. This data influx requires processing techniques, which are reproducible, accurate and fast, when the retrieval of information on plant growth and health status is envisaged.…”

Section: Introductionmentioning

confidence: 99%

On the Semi-Automatic Retrieval of Biophysical Parameters Based on Spectral Index Optimization

et al. 2014

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Regression models based on spectral indices are typically empirical formulae enabling the mapping of biophysical parameters derived from Earth Observation (EO) data. Due to its empirical nature, it remains nevertheless uncertain to what extent a selected regression model is the most appropriate one, until all band combinations and curve fitting functions are assessed. This paper describes the application of a Spectral Index (SI) assessment toolbox in the Automated Radiative Transfer Models Operator (ARTMO) package. ARTMO enables semi-automatic retrieval and mapping of biophysical parameters from optical remote sensing observations. The SI toolbox facilitates the assessment of biophysical parameter retrieval accuracy of established as well as new and generic SIs. For instance, based on the SI formulation used, all possible band combinations of formulations with up to ten bands can be defined and evaluated. Several options are available in the SI assessment: calibration/validation data partitioning, the addition of noise and the definition of curve fitting models. To illustrate its functioning, all two-band combinations according to simple ratio (SR) and normalized difference (ND) formulations as well as various fitting functions (linear, exponential, power, logarithmic, polynomial) functions led to similar best correlations, though spatial differences emerged when applying the functions to HyMap imagery. We suggest that a systematic SI assessment is a strong requirement in the quality assurance approach for accurate biophysical parameter retrieval.

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“…Since the pioneering mission Hyperion launched in 2001 [14] has opened new possibilities of global Earth coverage, hyperspectral imaging from a satellite platform has progressively grown in interest up to motivate the upcoming missions PRISMA [15] and EnMAP [16,17].…”

Section: Satellite Hyperspectral Processing Chainmentioning

confidence: 99%

Spectral Distortion in Lossy Compression of Hyperspectral Data

Aiazzi

Alparone

Baronti

et al. 2012

Journal of Electrical and Computer Engineering

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Distortion allocation varying with wavelength in lossy compression of hyperspectral imagery is investigated, with the aim of minimizing the spectral distortion between original and decompressed data. The absolute angular error, or spectral angle mapper (SAM), is used to quantify spectral distortion, while radiometric distortions are measured by maximum absolute deviation (MAD) for near-lossless methods, for example, differential pulse code modulation (DPCM), or mean-squared error (MSE) for lossy methods, for example, spectral decorrelation followed by JPEG 2000. Two strategies of interband distortion allocation are compared: given a target average bit rate, distortion may be set to be constant with wavelength. Otherwise, it may be allocated proportionally to the noise level of each band, according to the virtually lossless protocol. Comparisons with the uncompressed originals show that the average SAM of radiance spectra is minimized by constant distortion allocation to radiance data. However, variable distortion allocation according to the virtually lossless protocol yields significantly lower SAM in case of reflectance spectra obtained from compressed radiance data, if compared with the constant distortion allocation at the same compression ratio.

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The PRISMA payload optomechanical design, a high performance instrument for a new hyperspectral mission

Cited by 79 publications

References 0 publications

Reflectance Spectroscopy as a Tool for Monitoring Contaminated Soils

Reflectance Spectroscopy as a Tool for Monitoring Contaminated Soils

On the Semi-Automatic Retrieval of Biophysical Parameters Based on Spectral Index Optimization

Spectral Distortion in Lossy Compression of Hyperspectral Data

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