Spatial sampling, data models, spatial scale and ontologies: Interpreting spatial statistics and machine learning applied to satellite optical remote sensing

Peter M. Atkinson*, A. Stein, C. Jeganathan

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

6 Citations (Scopus)
148 Downloads (Pure)

Abstract

This paper summarizes the development and application of spatial statistical models in satellite optical remote sensing. The paper focuses on the development of a conceptual model that includes the measurement and sampling processes inherent in remote sensing. We organized this paper into five main sections: introducing the basis of remote sensing, including measurement and sampling; spatial variation, including variation through the object-based data model; advances in spatial statistical modelling; machine learning and explainable AI; a hierarchical ontological model of the nature of remotely sensed scenes. The paper finishes with a summary. We conclude that optical remote sensing provides an important source of data and information for the development of spatial statistical techniques that, in turn, serve as powerful tools to obtain important information from the images.

Original languageEnglish
Article number100646
Number of pages21
JournalSpatial statistics
Volume50
Early online date28 Feb 2022
DOIs
Publication statusPublished - Aug 2022

Keywords

  • Ontology
  • Remote sensing
  • Sampling
  • Scale
  • Spatial statistical modelling
  • UT-Hybrid-D
  • ITC-ISI-JOURNAL-ARTICLE

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