Unsupervised Semantic Labeling Framework for Identification of Complex Facilities in High-Resolution Remote Sensing Images

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.