Automatic spectral rule based preliminary mapping of calibrated Landsat TM and ETM+ Images (Articolo in rivista)

Type
Label
  • Automatic spectral rule based preliminary mapping of calibrated Landsat TM and ETM+ Images (Articolo in rivista) (literal)
Anno
  • 2006-01-01T00:00:00+01:00 (literal)
Alternative label
  • A. Baraldi , V. Puzzolo, P. Blonda, L. Bruzzone, C. Tarantino (2006)
    Automatic spectral rule based preliminary mapping of calibrated Landsat TM and ETM+ Images
    in IEEE transactions on geoscience and remote sensing
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • A. Baraldi , V. Puzzolo, P. Blonda, L. Bruzzone, C. Tarantino (literal)
Pagina inizio
  • 2563 (literal)
Pagina fine
  • 2586 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 44 (literal)
Rivista
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#descrizioneSinteticaDelProdotto
  • This work was originally carried out in the framework of the EU project entitled Landslide Early Warning Integrated Project (LEWIS) under Contract EVG1-CT-2001- 00055. CNR-ISSIA was a prtner of the project (literal)
Note
  • ISI Web of Science (WOS) (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • P. Blonda, primo ricercatore al CNR-ISSIA C. Tarantino, assegnista al CNR-ISSIA dal 2002 A. Baraldi, assegnista dell'ISSIA dal 2002 al 2005, attualmente lavora a contratto presso il JRC ad ISPRA V. Puzzolo lavora al JRC ad ISPRA L. Bruzzone, Univerità di Trento (literal)
Titolo
  • Automatic spectral rule based preliminary mapping of calibrated Landsat TM and ETM+ Images (literal)
Abstract
  • Based on purely spectral-domain prior knowledge taken from the remote sensing (RS) literature, an original spectral (fuzzy) rule-based per-pixel classifier is proposed. Requiring no training and supervision to run, the proposed spectral rule-based system is suitable for the preliminary classification (primal sketch, in the Marr sense) of Landsat-5 Thematic Mapper and Landsat- Enhanced Thematic Mapper Plus images calibrated into planetary reflectance (albedo) and at-satellite temperature. The classification system consists of a modular hierarchical top-down processing structure, which is adaptive to image statistics, computationally efficient, and easy to modify, augment, or scale to other sensors’ spectral properties, like those of the Advanced Spaceborne Thermal Emission and Reflection Radiometer and of the Satellite Pour l’Observation de la Terre (SPOT-4 and -5). As output, the proposed system detects a set of meaningful and reliable fuzzy spectral layers (strata) consistent (in terms of one-to-one or manyto- one relationships) with land cover classes found in levels I and II of the U.S. Geological Survey classification scheme. Although kernel spectral categories (e.g., strong vegetation) are detected without requiring any reference sample, their symbolic meaning is intermediate between those (low) of clusters and segments and those (high) of land cover classes (e.g., forest). This means that the application domain of the kernel spectral strata is by no means alternative to RS data clustering, image segmentation, and land cover classification. Rather, prior knowledge-based kernel spectral categories are naturally suitable for driving stratified application- specific classification, clustering, or segmentation of RS imagery that could involve training and supervision. The efficacy and robustness of the proposed rule-based system are tested in two operational RS image classification problems. (literal)
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