2006
DOI: 10.1109/tgrs.2006.874140
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Automatic Spectral Rule-Based Preliminary Mapping of Calibrated Landsat TM and ETM+ Images

Abstract: 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-7 Enhanced Thematic Mapper Plus images calibrated into planetary reflectance (albedo) and at-satellite temperature. The classifi… Show more

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Cited by 104 publications
(157 citation statements)
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“…(i) The year 2006 SIAM decision tree presented in Baraldi et al (2006). (ii) The static decision tree for Spectral Classification of surface reflectance signatures (SPECL) proposed by Dorigo et al (2009), see Table 4 in the Part 1 of this paper, and implemented by the Atmospheric/Topographic Correction for Satellite Imagery (ATCOR) commercial software product (Richter & Schläpfer, 2012a, 2012b).…”
Section: Methodsmentioning
confidence: 99%
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“…(i) The year 2006 SIAM decision tree presented in Baraldi et al (2006). (ii) The static decision tree for Spectral Classification of surface reflectance signatures (SPECL) proposed by Dorigo et al (2009), see Table 4 in the Part 1 of this paper, and implemented by the Atmospheric/Topographic Correction for Satellite Imagery (ATCOR) commercial software product (Richter & Schläpfer, 2012a, 2012b).…”
Section: Methodsmentioning
confidence: 99%
“…In each of its two independent sets of spectral rules for MS shape and MS intensity modelling SIAM pursues redundancy of spectral terms as a necessary condition to accomplish scalability to changes in the sensor spectral resolution specifications. Possible combinations of these two independent sets of spectral rules make the SIAM decision tree implementations, starting from that proposed in pseudo-code in Baraldi et al (2006), capable of representing the multivariate shape and multivariate intensity information components of a target MS hyperpolyhedron, neither necessarily convex nor connected, as a converging combination of independent functions whose individual terms are input with 1- to N -variate variables, with N equal to the total number of spectral channels. Multivariate data statistics are known to be more informative than a sequence of univariate data statistics.…”
Section: Methodsmentioning
confidence: 99%
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