Machine learning approaches for high-resolution urban land cover classification: a comparative study
Proceedings of the 2nd International Conference on Computing for Geospatial Research & Applications
Modeling spatial dependencies and semantic concepts in data mining
Proceedings of the 3rd International Conference on Computing for Geospatial Research and Applications
Aerial scene recognition using efficient sparse representation
Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing
Spatiotemporal data mining in the era of big spatial data: algorithms and applications
Proceedings of the 1st ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data
Object based image classification: state of the art and computational challenges
Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data
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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.