Literature DB >> 30035157

GEO-CEOS stage 4 validation of the Satellite Image Automatic Mapper lightweight computer program for ESA Earth observation level 2 product generation - Part 2: Validation.

Andrea Baraldi1,2,3,4, Michael Laurence Humber2, Dirk Tiede3, Stefan Lang3.   

Abstract

ESA defines as Earth Observation (EO) Level 2 information product a multi-spectral (MS) image corrected for atmospheric, adjacency, and topographic effects, stacked with its data-derived scene classification map (SCM), whose legend includes quality layers cloud and cloud-shadow. No ESA EO Level 2 product has ever been systematically generated at the ground segment. To fill the information gap from EO big data to ESA EO Level 2 product in compliance with the GEO-CEOS stage 4 validation (Val) guidelines, an off-the-shelf Satellite Image Automatic Mapper (SIAM) lightweight computer program was selected to be validated by independent means on an annual 30 m resolution Web-Enabled Landsat Data (WELD) image composite time-series of the conterminous U.S. (CONUS) for the years 2006 to 2009. The SIAM core is a prior knowledge-based decision tree for MS reflectance space hyperpolyhedralization into static (non-adaptive to data) color names. For the sake of readability, this paper was split into two. The present Part 2-Validation-accomplishes a GEO-CEOS stage 4 Val of the test SIAM-WELD annual map time-series in comparison with a reference 30 m resolution 16-class USGS National Land Cover Data (NLCD) 2006 map. These test and reference map pairs feature the same spatial resolution and spatial extent, but their legends differ and must be harmonized, in agreement with the previous Part 1 - Theory. Conclusions are that SIAM systematically delivers an ESA EO Level 2 SCM product instantiation whose legend complies with the standard 2-level 4-class FAO Land Cover Classification System (LCCS) Dichotomous Phase (DP) taxonomy.

Entities:  

Keywords:  Artificial intelligence; Cartesian product; binary relationship; cognitive science; color naming; connected-component multi-level image labeling; deductive inference; earth observation; high-level (attentive) and low-level (pre-attentional) vision; hybrid inference; image classification; image segmentation; inductive inference; land cover taxonomy; machine learning-from-data; outcome and process quality indicators; radiometric calibration; remote sensing; surface reflectance; thematic map comparison; top-of-atmosphere reflectance; two-way contingency table; unsupervised data discretization/vector quantization; validation

Year:  2018        PMID: 30035157      PMCID: PMC6036443          DOI: 10.1080/23312041.2018.1467254

Source DB:  PubMed          Journal:  Cogent Geosci        ISSN: 2331-2041


  3 in total

1.  Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy.

Authors:  Hanchuan Peng; Fuhui Long; Chris Ding
Journal:  IEEE Trans Pattern Anal Mach Intell       Date:  2005-08       Impact factor: 6.226

2.  Optimality of the basic colour categories for classification.

Authors:  Lewis D Griffin
Journal:  J R Soc Interface       Date:  2006-02-22       Impact factor: 4.118

3.  Parametric fuzzy sets for automatic color naming.

Authors:  Robert Benavente; Maria Vanrell; Ramon Baldrich
Journal:  J Opt Soc Am A Opt Image Sci Vis       Date:  2008-10       Impact factor: 2.129

  3 in total

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