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Service-oriented approach for geospatial feature discovery

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Abstract

Rapid increases in remote sensing capability have made remotely sensed images an importance source for intelligence analysts to discover geospatial features. The overwhelming volume of routine image acquisition has greatly outpaced the increase in the capacity of manual image interpretation by intelligence analysts, and prompted automated methods for geospatial feature extraction from high spatial resolution images. Nevertheless, existing methods focus on automatic extraction of isolated or elementary features, such as buildings and roads. A compound geospatial feature, such as a Weapon of Mass Destruction (WMD) proliferation facility, is spatially composed of elementary features (e.g., containment buildings, cooling ponds, and fences). The spatial relations among elementary features can assist the detection of compound features from images. This paper proposes a service-oriented approach for discovering compound geospatial features. The approach includes both a chaining strategy and an architecture. The chaining strategy is to discover sites of facilities by orchestrating services that compute spatial relations among elementary features. The architecture is a service-oriented framework to support the chaining for feature discovery. The approach not only takes advantages of spatial characteristics of complex features, but also enjoys the openness and flexibility of the Service-Oriented Architecture (SOA). A prototypical implementation is provided to illustrate the applicability of the approach.

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Acknowledgments

We are grateful to the anonymous reviewers, and to Dr. Barry Schlesinger for their valuable comments. This work was funded jointly by U.S. Department of Energy (grant #DE-NA0001123, PI: Prof. Liping Di), National Basic Research Program of China (2011CB707105), and Project 41023001 supported by NSFC.

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Authors and Affiliations

  1. Center for Spatial Information Science and Systems (CSISS), George Mason University, 10519 Braddock Road STE 2900, Fairfax, VA, 22032, USA

    Peng Yue, Liping Di, Weiguo Han, Peisheng Zhao, Wenli Yang & Lianlian He

  2. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, 129 Luoyu Road, 430079, Wuhan, China

    Peng Yue

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  1. Peng Yue
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Correspondence to Liping Di.

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Communicated by: Hassan Babaie

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Yue, P., Di, L., Han, W. et al. Service-oriented approach for geospatial feature discovery. Earth Sci Inform 5, 153–165 (2012). https://doi.org/10.1007/s12145-012-0104-0

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