Massively Parallel GPU Design of Automatic Target Generation Process in Hyperspectral Imagery

Abstract

A popular algorithm for hyperspectral image interpretation is the automatic target generation process (ATGP). ATGP creates a set of targets from image data in an unsupervised fashion without prior knowledge. It can be used to search a specific target in unknown scenes and when a target’s size is smaller than a single pixel. Its application has been demonstrated in many fields including geology, agriculture, and intelligence. However, the algorithm requires long time to process due to the massive amount of data. To expedite the process, the graphics processing units (GPUs) are an attractive alternative in comparison with traditional CPU architectures. In this paper, we propose a GPU-based massively parallel version of ATGP, which provides real-time performance for the first time in the literature. The HYDICE image data ( ${ {307\ast 307}}$ pixels and 210 spectral bands) are used for benchmark. Our optimization efforts on the GPU-based ATGP algorithm using one NVIDIA Tesla K20 GPU with I/O transfer can achieve a speedup of ${ {362\times}}$ with respect to its single-threaded CPU counterpart. We also tested the algorithm on Airborne Visible/InfraRed Imaging Spectrometer (AVIRIS) WTC dataset ( ${ {512\ast 614\ast 224}}$ of 224 bands) and Cuprite dataset ( ${{35\ast 350\ast 188}}$ of 188 bands), the speedup was ${ {416\times}}$ and ${ {320\times}}$ , respectively, when the target number was 15.