We present a highly scalable Monte Carlo (MC) three-dimensional photon transport simulation platform designed for heterogeneous computing systems. Through the development of a massively parallel MC algorithm using the Open Computing Language framework, this research extends our existing graphics processing unit (GPU)-accelerated MC technique to a highly scalable vendor-independent heterogeneous computing environment, achieving significantly improved performance and software portability. A number of parallel computing techniques are investigated to achieve portable performance over a wide range of computing hardware. Furthermore, multiple thread-level and device-level load-balancing strategies are developed to obtain efficient simulations using multiple central processing units and GPUs.
Hyperspectral images have traditionally been analyzed by pixel based methods. Invariant methods that consider
surface and shape geometry have not been used with these images. However, there is a need for such methods due
to the spectral and spatial variability present in these images. In this paper, we develop a method for classifying
these images invariant to translation and rotation. The method is based on developing shape descriptors using
spherical harmonics. These orthogonal functions have been widely used as a powerful tool for 3D shape recognition
and are better suited for hyperspectral images due to its inherent dimensionality. A spherical function defined on the
surface of a shape extracts rotation invariant features. In this case, the hyperspectral image is converted to spherical
coordinates, decomposed as a sum of its harmonics and then converted to Cartesian coordinates. A classifier is
trained with spherical harmonic descriptors computed from training samples. Support vector machines and
Maximum Likelihood are considered for classification. The method is tested with hyperspectral image from AISA,
AVIRIS and HYDICE sensors. The results show that the descriptors are effective in improving the accuracy of
classification.
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