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

Atkinson, P.M. and Stein, A. and Jeganathan, C. (2022) Spatial sampling, data models, spatial scale and ontologies : Interpreting spatial statistics and machine learning applied to satellite optical remote sensing. Spatial Statistics, 50: 100646. ISSN 2211-6753

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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.

Item Type:

Journal Article

Journal or Publication Title:

Spatial Statistics

Additional Information:

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

Uncontrolled Keywords:

/dk/atira/pure/subjectarea/asjc/1900/1903

Subjects:

?? ontologyremote sensingsamplingscalespatial statistical modellingcomputers in earth sciencesstatistics and probabilitymanagement, monitoring, policy and law ??

Departments:

Faculty of Science and Technology > Lancaster Environment Centre

ID Code:

168178

Deposited By:

ep_importer_pure

Deposited On:

30 Mar 2022 15:40

Refereed?:

Yes

Published?:

Published

Last Modified:

23 Oct 2024 00:05

URI:

https://eprints.lancs.ac.uk/id/eprint/168178