A Case-based Reasoning Approach for Task-driven Remote Sensing Image Discovery under Spatial-Temporal Constrains

LI Ming; ZHU Xinyan; DUAN Lian; GUO Wei; YAO Ming

doi:10.13203/j.whugis20140823

Volume 42 Issue 6

Jun. 2017

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Geomatics and Information Science of Wuhan University > 2017 > 42(6): 768-774. > DOI: 10.13203/j.whugis20140823

LI Ming, ZHU Xinyan, DUAN Lian, GUO Wei, YAO Ming. A Case-based Reasoning Approach for Task-driven Remote Sensing Image Discovery under Spatial-Temporal Constrains[J]. Geomatics and Information Science of Wuhan University, 2017, 42(6): 768-774. DOI: 10.13203/j.whugis20140823

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A Case-based Reasoning Approach for Task-driven Remote Sensing Image Discovery under Spatial-Temporal Constrains

LI Ming^{1, 2, 3, 4,},
ZHU Xinyan^2, ,,
DUAN Lian^{3, 4},
GUO Wei²,
YAO Ming¹

1.
Institute of Space Science and Technology, Nanchang University, Nanchang 330031, China
2.
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
3.
Key Laboratory of Environment Change and Resource Use in Beibu Gulf of Ministry of Education, Guangxi Teachers Education University, Nanning 530001, China
4.
Guangxi Key Laboratory of Earth Surface Processes and Intelligent Simulation, Guangxi Teachers Education University, Nanning 530001, China

Funds:

The National Basic Research Program of China 2012BAH35B03

the National Natural Science Foundation of China 41201405

the Open Research Fund of State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing 16 (Key 04)

the Special Research Fund of State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing

the Open Fund of Guangxi Key Laboratory of Earth Surface Processes and Intelligent Simulation 2015GXESPKF02

the Natural Science Foundation of Jiangxi Province 20151BAB207004

More Information

Author Bio:
LI Ming, PhD, specializes in the intelligent geospatial information service. E-mail:liming10307@163.com
Corresponding author:
ZHU Xinyan, PhD, professor. E-mail:geozxy@263.net
Received Date: December 21, 2015
Published Date: June 04, 2017

Graphical Abstract

Abstract

Abstract

Remote sensing (RS) images are an important source of geospatial data. However, current approaches in task-driven RS images discovery establish links between tasks and RS image parameters directly, without spatial-temporal constraints, leading to hard maintenance and low query precision Moreover, the complex relationship between tasks and RS images under spatial-temporal constraints is difficult to model and represent by rules. Thus, this research proposes an location and time method that not only filters but also acts as spatial-temporal constraint in the discovery process, and exploits the relationships between tasks and RS data sources under spatial-temporal constraints through Case-based Reasoning (CBR). The RS application case representation model and similarity assessment model is proposed to support analogical reasoning in CBR. A prototype system was developed to validate this method. The results show that the method is a feasible approach that improves the service efficacy of remote sensing data.
- remote sensing image discovery,
- task-driven,
- case-based reasoning,
- analogical reasoning,
- spatial data searching

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