Data and Information Quality in Remote Sensing

John Puentes (1, 2) , Laurent Lecornu (1, 2) , Basel Solaiman (2)

1 Lab-STICC_IMTA_CID_DECIDE
2 IMT Atlantique - ITI - Département lmage et Traitement Information

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.

Mots clés: Remote sensing; Acquisition-decision process; Geographic information systems; Data-information fusion quality

Domaines: Vision par ordinateur et reconnaissance de formes [cs.CV]; Traitement du signal et de l'image [eess.SP]

John Puentes, Laurent Lecornu, Basel Solaiman. Data and Information Quality in Remote Sensing. Information Quality in Information Fusion and Decision Making, Springer Nature, pp.401 - 421, 2019, Information Fusion and Data Science, 978-3-030-03642-3. ⟨10.1007/978-3-030-03643-0_17⟩. ⟨hal-02128849⟩