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Characterizing vegetation phenology is a highly significant problem, due to its importance in regulating ecosystem carbon cycling, interacting with climate changes, and decision-making of croplands managements. While ground based sensors, such as the AmeriFlux sensors, can provide measurements at high temporal resolution (every hour) and can be used to accurately calculate vegetation phenology indices,...
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...
Human settlement regions with different physical and socio-economic attributes exhibit unique spatial characteristics that are often illustrated in high-resolution overhead imageries. For example-size, shape and spatial arrangements of man-made structures are key attributes that vary with respect to the socio-economic profile of the neighborhood. Successfully modeling these attributes is crucial in...
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 a supervised semantic labeling framework based on the Latent Dirichlet Allocation method. This framework is used to analyze over 120 images collected under different spatial and temporal settings over...
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...
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