Knowledge representation of remote sensing quantitative retrieval models

Zhang, Jingzun, Xue, Yong, Dong, Jing, Liu, Jia, Liu, Longli, Siva, Sahithi and Guang, Jie (2014) Knowledge representation of remote sensing quantitative retrieval models. In: 2014 IEEE Geoscience and Remote Sensing Symposium, 13-18 July 2014, QUÉBEC CITY, CANADA.

[img]
Preview
Text
Sahithi-edited.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.

Download (407kB) | Preview
Official URL: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&ar...

Abstract / Description

A large number of quantitative retrieval models have been proposed in recent years, and there is continuous momentum in proposing new ones. Building a model, from design through to implementation stages, involves a process of knowledge collection, organization and transmission. In this paper we introduce the SECI model to manage the conversion of qualitative remote sensing knowledge and propose a mode of knowledge representation on the basis of the ontology for geospatial modeling. We develop a platform based on the above research and demonstrate the efficiency of the knowledge representation mode using this platform.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: explicit knowledge, tacit knowledge, quantitative retrieval models, workflow, Ontology, Remote Sensing
Subjects: 000 Computer science, information & general works
Department: School of Computing and Digital Media
Depositing User: Bal Virdee
Date Deposited: 21 Feb 2019 16:12
Last Modified: 21 Feb 2019 16:12
URI: http://repository.londonmet.ac.uk/id/eprint/4635

Actions (login required)

View Item View Item