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. Issue 1 (1st January 2018)

• Record Type: Journal Article
• Authors: Baraldi, Andrea; Humber, Michael Laurence; Tiede, Dirk; Lang, Stefan
• Editors: Moresi, Louis-Noel
• Is Part Of: Cogent geoscience. Volume 4:Issue 1(2018)
• Publication Date: 2018-01-01
• Subjects: Artificial intelligence -- binary relationship -- Cartesian product -- cognitive science -- color naming -- connected-component multi-level image labeling -- deductive inference -- earth observation -- land cover taxonomy -- high-level (attentive) and low-level (pre-attentional) vision -- hybrid inference -- image classification -- image segmentation -- inductive inference -- 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
• DOI: 10.1080/23312041.2018.1467254
• Languages: English
• ISSNs: 2331-2041

Abstract

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.