An Ontology-Based Framework for Integrating Remote Sensing Imagery, Image Products, and In Situ Observations

Chao Wang,Wei Wang,Zeqiang Chen,Xinyan Zhuo,Pengfei Li,Nengcheng Chen,Davide Palumbo

doi:10.1155/2020/6912820

Abstract

Along with the rapid development of remote sensing satellites and sensor network technology, vast amounts of remote sensing imagery and in situ observations have been accumulated. Further, various researchers and agencies have released a variety of thematic image products. These heterogeneous observations are therefore difficult to utilize comprehensively. In this study, an ontology-based framework for integrating remote sensing imagery, image products, and in situ observations was developed. It was extended based on the Semantic Sensor Network (SSN) ontology in the Web Ontology Language (OWL). The detailed process of ontology construction and rule establishment was demonstrated. Combined with some actual remote sensing imagery, image products, and in situ observations, semantic queries based on DL Query and SPARQL were conducted to establish the rationality and feasibility of the ontology and framework.

Highlights

With the development of remote sensing technology and the improvement of the resolution of satellite sensors, the application of remote sensing to quantitatively obtain the required parameters on a large scale on the ground has become even more extensive
Considering the abovementioned themes, the objective of this study was to develop an ontology with semantic rules to integrate remote sensing imagery, image products, and in situ observations based on the W3C OWL 2 Web Ontology Language [33] and the software Protégé developed by the Stanford Center for Biomedical Informatics Research at the Stanford University School of Medicine [34]
HermiT was selected as the reasoner, and the rules for reasoning were encoded in Semantic Web Rule Language (SWRL) [61] and implemented by SWRL Tab Protégé 5.0 + Plugin in Protégé

Summary

Introduction

With the development of remote sensing technology and the improvement of the resolution of satellite sensors, the application of remote sensing to quantitatively obtain the required parameters on a large scale on the ground has become even more extensive. As a kind of ground-based monitoring system, wireless sensor networks and remote sensing data functionally complement each other and can dynamically monitor all kinds of parameters needed by the region in quasi-real time. A large number of studies have focused on integrating remote sensing and ground-based sensor networks [5,6,7,8,9,10]. From the contents of these studies, it can be seen that research on integrating remote sensing images and ground sensor networks is often aimed at a single target, Journal of Sensors using specific remote sensing images. It is difficult to carry out composite and integrated research

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Conclusion