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

Autor: Andrea, Baraldi, Michael Laurence, Humber, Dirk, Tiede, Stefan, Lang
Jazyk: angličtina
Rok vydání: 2018
Předmět:
outcome and process quality indicators
Artificial intelligence
Cognitive Artificial Intelligence
deductive inference
Image Processing
Algorithms & Complexity
lcsh:QC851-999
Intelligent Systems
color naming
binary relationship
Machine Learning
Automation
high-level (attentive) and low-level (pre-attentional) vision
remote sensing
Expert systems
top-of-atmosphere reflectance
thematic map comparison
Cartesian product
inductive inference
Real-Time Systems
Quality Control & Reliability
image segmentation
unsupervised data discretization/vector quantization
validation
Earth observation
surface reflectance
cognitive science
lcsh:QE1-996.5
lcsh:QC801-809
Systems Architecture
radiometric calibration
machine learning-from-data
connected-component multilevel image labeling
lcsh:Geology
two-way contingency table
lcsh:Geophysics. Cosmic physics
hybrid inference
Pattern Analysis
GIS
Remote Sensing & Cartography
lcsh:Meteorology. Climatology
land cover taxonomy
Research Article
image classification
Zdroj: Cogent Geoscience, Vol 4, Iss 1 (2018)
Cogent Geoscience
ISSN: 2331-2041
Popis: 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.
Databáze: OpenAIRE
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