GEOSPATIAL IMAGE MINING FOR NUCLEAR PROLIFERATION DETECTION: CHALLENGES AND NEW OPPORTUNITIES

被引:18
作者
Vatsavai, Ranga Raju [1 ]
Bhaduri, Budhendra [1 ]
Cheriyadat, Anil [1 ]
Arrowood, Lloyd [2 ]
Bright, Eddie [1 ]
Gleason, Shaun [1 ]
Diegert, Carl [5 ]
Katsaggelos, Aggelos [7 ]
Pappas, Thrasos [7 ]
Porter, Reid
Bollinger, Jim [3 ]
Chen, Barry [4 ]
Hohimer, Ryan [6 ]
机构
[1] Oak Ridge Natl Lab, Oak Ridge, TN 37831 USA
[2] Y12 Natl Secur Complex, Oak Ridge, TN 37831 USA
[3] Savannah River Natl Lab, Aiken, SC 29808 USA
[4] Lawrence Livermore Natl Lab, Livermore, CA 94550 USA
[5] Sandia Natl Labs, Albuquerque, NM 87123 USA
[6] Pacific Northwest Natl Lab, Richland, WA 99354 USA
[7] Argonne Natl Lab, Argonne, IL 60439 USA
来源
2010 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM | 2010年
关键词
Nuclear proliferation; low-level features; semantic classification; geospatial ontology; SALIENCY;
D O I
10.1109/IGARSS.2010.5649811
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
With increasing understanding and availability of nuclear technologies, and increasing persuasion of nuclear technologies by several new countries, it is increasingly becoming important to monitor the nuclear proliferation activities. There is a great need for developing technologies to automatically or semi-automatically detect nuclear proliferation activities using remote sensing. Images acquired from earth observation satellites is an important source of information in detecting proliferation activities. High-resolution remote sensing images are highly useful in verifying the correctness, as well as completeness of any nuclear program. DOE national laboratories are interested in detecting nuclear proliferation by developing advanced geospatial image mining algorithms. In this paper we describe the current understanding of geospatial image mining techniques and enumerate key gaps and identify future research needs in the context of nuclear proliferation.
引用
收藏
页码:48 / 51
页数:4
相关论文
共 6 条
[1]  
[Anonymous], 2008, OBJECT BASED IMAGE A, DOI DOI 10.1007/978
[2]   INCREMENTAL PARSING FOR LATENT SEMANTIC INDEXING OF IMAGES [J].
Bae, Soo Hyun ;
Juang, Biing-Hwang .
2008 15TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOLS 1-5, 2008, :925-928
[3]   A model of saliency-based visual attention for rapid scene analysis [J].
Itti, L ;
Koch, C ;
Niebur, E .
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 1998, 20 (11) :1254-1259
[4]  
Jasani B, 2009, INT SAFEGUARDS SATEL
[5]   Saliency, scale and image description [J].
Kadir, T ;
Brady, M .
INTERNATIONAL JOURNAL OF COMPUTER VISION, 2001, 45 (02) :83-105
[6]  
Vijayaraj V., 2008, PROC 37 IEEE APPL IM, P1