Information-theoretic assessment of optical remote-sensing imagery (Contributo in atti di convegno)

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  • Information-theoretic assessment of optical remote-sensing imagery (Contributo in atti di convegno) (literal)
Anno
  • 2004-01-01T00:00:00+01:00 (literal)
Alternative label
  • B. Aiazzi, L. Alparone, S. Baronti, C. Lastri, L. Santurri, M. Selva (2004)
    Information-theoretic assessment of optical remote-sensing imagery
    in ESA EUSC 2004, Madrid, Spain, 17-18 Mar. 2004
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • B. Aiazzi, L. Alparone, S. Baronti, C. Lastri, L. Santurri, M. Selva (literal)
Pagina inizio
  • 5 (literal)
Pagina fine
  • 12 (literal)
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  • Proceedings ESA-EUSC 2004 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • SP-553 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#volumeInCollana
  • SP-553 (literal)
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  • Theory and Applications of Knowledge-Driven Image Information Mining with Focus on Earth Observation, Madrid, Spain, 17-18 Mar. 2004, ESA Special Publication No. 553, Sep. 2004 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#pagineTotali
  • 8 (literal)
Note
  • Google Scholar (literal)
  • Scopu (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • B. Aiazzi, L. Alparone, S. Baronti, C. Lastri, L. Santurri, M. Selva: IFAC-CNR. L. Alparone: DET-UniFI: Department of Electronics and Telecommunications, University of Firenze Via Santa Marta, 3, 50139 Firenze (Italy), Alparone@lci.det.unifi.it (literal)
Titolo
  • Information-theoretic assessment of optical remote-sensing imagery (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn
  • 92-9092-864-6 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#curatoriVolume
  • H. Lacoste & L. Ouwehand (literal)
Abstract
  • This work focuses on estimating the information conveyed to a user by multi-band remotely sensed optical data, either multi-spectral or hyper-spectral. A trade-off exists between spatial and spectral resolution, due to physical constraints of sensors imaging with a prefixed SNR. Lossless data compression is exploited to measure the useful information content of the data. The bit-rate achieved by the reversible compression process takes into account both the contribution of the \"observation\" noise, i.e. information regarded as statistical uncertainty, whose relevance is null to a user, and the intrinsic information of hypothetically noise-free radiance data. An entropy model of the image source is defined and, once the standard deviation of the noise, assumed to be Gaussian, has been preliminary measured, such a model is inverted to yield an estimate of the information content of the noise-free source from the code rate. Results of mutual information assessment are reported and discussed on Landsat TM data and on AVIRIS data. (literal)
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