Geo-processing workflow driven wildfire hot pixel detection under sensor web environment
Geo-processing workflow driven wildfire hot pixel detection under sensor web environment
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DOI：
10.1016/j.cageo.2009.06.013
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发表时间：
2010-03-01
影响因子：
4.4
通讯作者：
Gong, Jianya
中科院分区：
文献类型：
--
作者：
Chen, Nengcheng;Di, Liping;Gong, Jianya
将传感器Web使能（SWE）服务与地理处理工作流（GPW）集成已成为基于传感器Web的应用，特别是遥感观测的瓶颈。本文提出了一个通用的GPW框架传感器Web数据服务的一部分，美国宇航局传感器Web项目。该抽象框架包括抽象GPW模型构造、服务组合的GPW链和数据检索组件。具体框架由数据服务节点、数据处理节点、数据表示节点、目录服务节点和查询引擎组成。采用抽象模型设计器设计GPW的顶层模型，使用模型实例化服务生成具体的业务流程执行语言，并采用业务流程执行引擎。该框架用于生成多种数据：来自实时传感器的原始数据、覆盖或特征数据、地理空间产品或传感器地图。给出了一个原型系统，包括模型设计器、模型实例化服务和GPW引擎BPELPower。一个场景的EO-1传感器网络数据服务野火热像素检测被用来测试所提出的框架的可行性。评估了EO-1实时数据野火分类服务框架的执行时间和影响。所提出的框架的好处和高性能进行了讨论。通过EO-1卫星实况数据野火分类服务的实验表明，该框架能够提高传感器数据检索和处理的服务质量。爱思唯尔有限公司出版
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.