Quality and information content of CHRIS hyper-spectral data (Contributo in atti di convegno)

  • Quality and information content of CHRIS hyper-spectral data (Contributo in atti di convegno) (literal)
  • 2005-01-01T00:00:00+01:00 (literal)
Alternative label
  • B. Aiazzi, S. Baronti, P. Marcoionni, I. Pippi, M. Selva (2005)
    Quality and information content of CHRIS hyper-spectral data
    in 3rd CHRIS/Proba Workshop, Frascati, Italia, 21-23 Marzo 2005
  • B. Aiazzi, S. Baronti, P. Marcoionni, I. Pippi, M. Selva (literal)
Pagina inizio
  • 15 (literal)
Pagina fine
  • 24 (literal)
  • Proc. 3rd CHRIS/Proba Workshop, ESRIN, Frascati, Italy, 21-23 Mar. 2005, ESA SP No. 593, June 2005 (literal)
  • http://earth.esa.int/workshops/chris_proba_05/papers/07_aiazzi.pdf (literal)
  • Proceedings of Third CHRIS/Proba Workshop (literal)
  • SP-593 (literal)
  • SP-593 (literal)
  • Accessibile in linea presso: http://earth.esa.int/workshops/chris_proba_05/papers/07_aiazzi.pdf (literal)
  • 10 (literal)
  • Scopu (literal)
  • IFAC-CNR (literal)
  • Quality and information content of CHRIS hyper-spectral data (literal)
  • 92-9092-904-9 (literal)
  • H. Lacoste (literal)
  • The work focuses on evaluating quality and estimating information of CHRIS hyper-spectral images. Quality is assessed through the characterisation of the noise while information is estimated by means of an operative definition according to which the information content of a data set is given by the amount of information that cannot be predicted from the data that have already been acquired and, thus, by the entropy of the prediction errors. The noise model is first verified and the parameters of the model are then estimated. Afterwards lossless data compression is exploited to measure the entropy of the prediction errors through their bit-rate. The information content of the data is estimated by taking into account that the bit-rate achieved by the reversible compression process is due to both the contribution of the noise, whose relevance is null to a user, and of the hypothetically noise-free data. Since our goal is to estimate the amount of information of the ideal noise-free data, an entropy-variance model is assumed for the ideal image. Once all the parameters of the model have been estimated, the entropy of the noise-free source is derived. Results are reported and discussed for hyper-spectral data sets acquired by CHRIS spectrometer. Information assessment is first assessed before and after the radiometric correction process in order to evaluate any effect introduced in the processed data. Then different areas of the same image are processed in order to assess the noise model. Eventually, the procedure is utilised to characterise data sets that have been acquired in different times in order to verify and assess any potential operational change occurred in the instrument set-up or in the processing chain. (literal)
Prodotto di
Autore CNR
Insieme di parole chiave

Incoming links:

Autore CNR di
Editore di
Insieme di parole chiave di