Title:	Spectral rule-based expert system for automatic near real-time thermal anomalies detection in geostationary GOES-16 ABI imagery
Authors:	Carvalho, Luiz F. R. de Laneve, Giovanni Baraldi, Andrea Santilli, Giancarlo
Assunto::	Sistemas especializados Processamento de imagens geofísicas Sensoriamento remoto
Issue Date:	2021
Publisher:	IEEE
Citation:	CARVALHO, Luiz F. R. de et al. Spectral rule-based expert system for automatic near real-time thermal anomalies detection in geostationary GOES-16 ABI imagery. 2021 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM IGARSS, 2021, Brussels - Belgium. DOI: 10.1109/IGARSS47720.2021.9553625.
Abstract:	Typical advantages and limitations of prior knowledge-based (deductive, top-down) expert systems are well known in literature: they typically score “high” in efficiency and interpretability, but they tend to score “low” in transferability/robustness to changes in input data. To benefit from these advantages while overcoming their typical shortcomings, an original expert system, based on a priori purely spectral-domain knowledge, is proposed for per-pixel (spatial context-insensitive) automatic near real-time detection of thermal anomalies in geostationary GOES-16 ABI multi-spectral (MS) imagery. Unable to learn-from-data, the proposed static decision-tree for MS signature recognition (classification) requires neither training data nor human-machine interaction to run. Its degrees of novelty pertain to the Marr levels of system understanding known as information/knowledge representation, system design (architecture) and implementation. Input with day and night ABI imagery acquired every 15 minutes, the proposed expert system detected 680 pixels with thermal anomalies in ABI images of the North and South Americas acquired from 30/01/2018 (15:00 UTC) to 31/01/2018 (01:30 UTC).
metadata.dc.description.unidade:	Faculdade UnB Gama (FGA)
DOI:	10.1109/IGARSS47720.2021.9553625
metadata.dc.relation.publisherversion:	https://ieeexplore.ieee.org/document/9553625
Appears in Collections:	Trabalhos apresentados em evento

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