Unsupervised Semantic Labeling Framework for Identification of Complex Facilities in High-Resolution Remote Sensing Images
- 1 December 2010
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 273-280
- https://doi.org/10.1109/icdmw.2010.151
Abstract
Nuclear proliferation is a major national security concern for many countries. Existing feature extraction and classification approaches are not suitable for monitoring proliferation activity using high-resolution multi-temporal remote sensing imagery. In this paper we present an unsupervised semantic labeling framework based on the Latent Dirichlet Allocation method. This framework is used to analyze over 70 images collected under different spatial and temporal settings over the globe representing two major semantic categories: nuclear and coal power plants. Initial experimental results show a reasonable discrimination of these two categories even though they share highly overlapping and common objects. This research also identified several research challenges associated with nuclear proliferation monitoring using high resolution remote sensing images.Keywords
This publication has 11 references indexed in Scilit:
- Geospatial image mining for nuclear proliferation detection: Challenges and new opportunitiesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2010
- Semantic Annotation of Satellite Images Using Latent Dirichlet AllocationIEEE Geoscience and Remote Sensing Letters, 2009
- An efficient spatial semi-supervised learning algorithmInternational Journal of Parallel, Emergent and Distributed Systems, 2007
- A Bayesian Hierarchical Model for Learning Natural Scene CategoriesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- Large-Scale Geospatial Indexing for Image-Based Retrieval and AnalysisLecture Notes in Computer Science, 2005
- Classifying land development in high-resolution panchromatic satellite images using straight-line statisticsIEEE Transactions on Geoscience and Remote Sensing, 2004
- Finding scientific topicsProceedings of the National Academy of Sciences of the United States of America, 2004
- Retrieval of translated, rotated and scaled color texturesPattern Recognition, 2003
- Rotation and gray scale transform invariant texture identification using wavelet decomposition and hidden Markov modelIeee Transactions On Pattern Analysis and Machine Intelligence, 1994
- Extracting Straight LinesIeee Transactions On Pattern Analysis and Machine Intelligence, 1986