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

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.

I. Introduction

Hyperspectral imaging is concerned with the measurement, analysis, and interpretation of spectra acquired from a given scene at a given distance by a satellite sensor. Two systems currently active and operated from airborne platforms are NASA Jet Propulsion Laboratory’s Airborne Visible/InfraRed Imaging Spectrometer (AVIRIS) and Naval Research Laboratory’s HYDICE sensor. Many more are under development. HYDICE sensor was developed by Hughes Danbury Optical Systems. Hyperspectral sensors are widely used in many fields such as geology, agriculture, and intelligence. A significant number of researchers work on hyperspectral image processing, such as automatic spectral target recognition (ASTR), image classification, and image fusion [1]–[6]. The major advantage of a hyperspectral sensor is its significantly improved spectral and spatial resolution. These improvements also mean that many unknown signals can be uncovered as anomalies without prior knowledge. This has significantly expanded the domain of many analysis techniques. The sensors have also been shown to detect targets with size smaller than a single pixel. In order to detect these targets, one must rely on their spectral properties and identify them on subpixel scale: a task that cannot be accomplished using traditional spatial-based image processing techniques. Real-time or nearly real-time processing of hyperspectral images is required for swift decisions, and it depends on fast data processing.