Geo-processing workflow driven wildfire hot pixel detection under sensor web environment

被引:47
作者
Chen, Nengcheng [1 ,2 ]
Di, Liping [1 ]
Yu, Genong [1 ]
Gong, Jianya [2 ]
机构
[1] George Mason Univ, CSISS, Greenbelt, MD 20770 USA
[2] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Peoples R China
关键词
Sensor Web; Geo-Processing Workflow; Business Process Execution Language; Geo-Processing Model; Earth Observation 1; Wildfire hot pixel detection;
D O I
10.1016/j.cageo.2009.06.013
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Integrating Sensor Web Enablement (SWE) services with Geo-Processing Workflows (GPW) has become a bottleneck for Sensor Web-based applications, especially remote-sensing observations. This paper presents a common GPW framework for Sensor Web data service as part of the NASA Sensor Web project. This abstract framework includes abstract GPW model construction, GPW chains from service combination, and data retrieval components. The concrete framework consists of a data service node, a data processing node, a data presentation node, a Catalogue Service node, and a BPEL engine. An abstract model designer is used to design the top level GPW model, a model instantiation service is used to generate the concrete Business Process Execution Language (BPEL), and the BPEL execution engine is adopted. This framework is used to generate several kinds of data: raw data from live sensors, coverage or feature data, geospatial products, or sensor maps. A prototype, including a model designer, model instantiation service, and GPW engine-BPELPower is presented. A scenario for an EO-1 Sensor Web data service for wildfire hot pixel detection is used to test the feasibility of the proposed framework. The execution time and influences of the EO-1 live Hyperion data wildfire classification service framework are evaluated. The benefits and high performance of the proposed framework are discussed. The experiments of EO-1 live Hyperion data wildfire classification service show that this framework can improve the quality of services for sensor data retrieval and processing. Published by Elsevier Ltd.
引用
收藏
页码:362 / 372
页数:11
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