Stage 4 validation of the Satellite Image Automatic Mapper lightweight computer program for Earth observation Level 2 product generation, Part 2 Validation

The European Space Agency (ESA) defines an Earth Observation (EO) Level 2 product as a multispectral (MS) image corrected for geometric, atmospheric, adjacency and topographic effects, stacked with its scene classification map (SCM) whose legend includes quality layers such as cloud and cloud-shadow. No ESA EO Level 2 product has ever been systematically generated at the ground segment. To contribute toward filling an information gap from EO big sensory data to the ESA EO Level 2 product, a Stage 4 validation (Val) of an off the shelf Satellite Image Automatic Mapper (SIAM) lightweight computer program for prior knowledge based MS color naming was conducted by independent means. A time-series of annual Web Enabled Landsat Data (WELD) image composites of the conterminous U.S. (CONUS) was selected as input dataset. The annual SIAM WELD maps of the CONUS were validated in comparison with the U.S. National Land Cover Data (NLCD) 2006 map. These test and reference maps share the same spatial resolution and spatial extent, but their map legends are not the same and must be harmonized. For the sake of readability this paper is split into two. The previous Part 1 Theory provided the multidisciplinary background of a priori color naming. The present Part 2 Validation presents and discusses Stage 4 Val results collected from the test SIAM WELD map time series and the reference NLCD map by an original protocol for wall to wall thematic map quality assessment without sampling, where the test and reference map legends can differ in agreement with the Part 1. Conclusions are that the SIAM-WELD maps instantiate a Level 2 SCM product whose legend is the FAO Land Cover Classification System (LCCS) taxonomy at the Dichotomous Phase (DP) Level 1 vegetation/nonvegetation, Level 2 terrestrial/aquatic or superior LCCS level.

Stage 4 validation of the Satellite Image Automatic Mapper lightweight computer program for Earth observation Level 2 product generation, Part 1 Theory

The European Space Agency (ESA) defines an Earth Observation (EO) Level 2 product as a multispectral (MS) image corrected for geometric, atmospheric, adjacency and topographic effects, stacked with its scene classification map (SCM), whose legend includes quality layers such as cloud and cloud-shadow. No ESA EO Level 2 product has ever been systematically generated at the ground segment. To contribute toward filling an information gap from EO big data to the ESA EO Level 2 product, an original Stage 4 validation (Val) of the Satellite Image Automatic Mapper (SIAM) lightweight computer program was conducted by independent means on an annual Web-Enabled Landsat Data (WELD) image composite time-series of the conterminous U.S. The core of SIAM is a one pass prior knowledge based decision tree for MS reflectance space hyperpolyhedralization into static color names presented in literature in recent years. For the sake of readability this paper is split into two. The present Part 1 Theory provides the multidisciplinary background of a priori color naming in cognitive science, from linguistics to computer vision. To cope with dictionaries of MS color names and land cover class names that do not coincide and must be harmonized, an original hybrid guideline is proposed to identify a categorical variable pair relationship. An original quantitative measure of categorical variable pair association is also proposed. The subsequent Part 2 Validation discusses Stage 4 Val results collected by an original protocol for wall-to-wall thematic map quality assessment without sampling where the test and reference map legends can differ. Conclusions are that the SIAM-WELD maps instantiate a Level 2 SCM product whose legend is the 4 class taxonomy of the FAO Land Cover Classification System at the Dichotomous Phase Level 1 vegetation/nonvegetation and Level 2 terrestrial/aquatic.