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 Baraldi; Michael Laurence Humber; Dirk Tiede; Stefan Lang

doi:10.1080/23312041.2018.1467254

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

Cogent Geosci. 2018 Jun 11;4(1):1467254. doi: 10.1080/23312041.2018.1467254. eCollection 2018.

Authors

Andrea Baraldi^{1

2

3

4}, Michael Laurence Humber², Dirk Tiede³, Stefan Lang³

Affiliations

¹ Department of Agricultural Sciences, University of Naples Federico II, Portici, Italy.
² Department of Geographical Sciences, University of Maryland, College Park, MD, USA.
³ Department of Geoinformatics - Z_GIS, University of Salzburg, Salzburg, Austria.
⁴ Italian Space Agency (ASI), Rome, Italy.

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.

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.

Grants and funding

To accomplish this work, Andrea Baraldi was supported in part by the National Aeronautics and Space Administration (NASA) under Grant No. NNX07AV19G issued through the Earth Science Division of the Science Mission Directorate. Andrea Baraldi, Dirk Tiede, and Stefan Lang were supported in part by the Austrian Science Fund (FWF) through the Doctoral College GIScience (DK W1237- N23).