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Erschienen in:
Information Quality in Fusion-Driven Human-Machine Environments Kapitel lesen Erstes Kapitel lesen

2019 | OriginalPaper | Buchkapitel

17. Data and Information Quality in Remote Sensing

verfasst von : John Puentes, Laurent Lecornu, Basel Solaiman

Erschienen in: Information Quality in Information Fusion and Decision Making

Verlag: Springer International Publishing

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Abstract

Remote sensing datasets are characterized by multiple types of imperfections that alter extracted information and taken decisions to a variable degree depending on data acquisition conditions, processing, and final product requirements. Therefore, regardless of the sensors, type of data, extracted information, and complementary algorithms, the quality assessment question is a pervading and particularly complex one. This chapter summarizes relevant quality assessment approaches that have been proposed for data acquisition, information extraction, and data and information fusion, of the remote sensing acquisition-decision process. The case of quality evaluation for geographic information systems, which make use of remote sensing products, is also described. Aspects of a comprehensive quality model for remote sensing and problems that remain to be addressed offer a perspective of possible evolutions in the field.

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Vorheriges Kapitel An Epistemological Model for a Data Analysis Process in Support of Verification and Validation
Nächstes Kapitel Reliability-Aware and Robust Multi-sensor Fusion Toward Ego-Lane Estimation Using Artificial Neural Networks
Fußnoten
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Metadaten
Titel
Data and Information Quality in Remote Sensing
verfasst von
John Puentes
Laurent Lecornu
Basel Solaiman
Copyright-Jahr
2019
Buch
Information Quality in Information Fusion and Decision Making

Print ISBN: 978-3-030-03642-3

Electronic ISBN: 978-3-030-03643-0

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-030-03643-0_17