Home > Research > Publications & Outputs > Spatial sampling, data models, spatial scale an...

Research

Associated organisational units

Electronic data

  • Binder1

    Rights statement: This is the author’s version of a work that was accepted for publication in Spatial Statistics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Spatial Statistics, 50, 100646, 2022 DOI: 10.1016/j.spasta.2022.100646

    Accepted author manuscript, 394 KB, PDF document

    Available under license: CC BY-NC-ND: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License

Links

Text available via DOI:

Keywords

View graph of relations

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

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Published
Close
More...
Article number100646
<mark>Journal publication date</mark>31/08/2022
<mark>Journal</mark>Spatial Statistics
Volume50
Number of pages21
Publication StatusPublished
Early online date28/02/22
<mark>Original language</mark>English

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.

Bibliographic note

This is the author’s version of a work that was accepted for publication in Spatial Statistics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Spatial Statistics, 50, 100646, 2022 DOI: 10.1016/j.spasta.2022.100646