GEO-CEOS stage 4 validation of the Satellite Image Automatic Mapper lightweight computer program for ESA Earth observation level 2 product generation - Part 1: Theory. 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 multilevel 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: 10.1080/23312041.2018.1467357
• Languages: English
• ISSNs: 2331-2041

Abstract

Abstract: ESA defines as Earth Observation (EO) Level 2 information product a single-date 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 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 2006–2009. The SIAM core is a prior knowledge-based decision tree for MS reflectance space hyperpolyhedralization into static color names. Typically, a vocabulary of MS color names in a MS data (hyper)cube and a dictionary of land cover (LC) class names in the scene-domain do not coincide and must be harmonized (reconciled). The present Part 1—Theory provides the multidisciplinary background of a priori color naming. The subsequent 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 2006 map, based on an original protocol for wall-to-wall thematic map quality assessment without sampling, where the test and reference maps feature the same spatial resolution and spatial extent, but whose legends differ and must be harmonized.