High Performance Hyperspectral Image Classification using Graphics Processing Units

Abstract

Real-time remote sensing applications like search and rescue missions, military target detection, environmental monitoring, hazard prevention and other time-critical applications require onboard real time processing capabilities or autonomous decision making. Some unmanned remote systems like satellites are physically remote from their operators, and all control of the spacecraft and data returned by the spacecraft must be transmitted over a wireless radio link. This link may not be available for extended periods when the satellite is out of line of sight of its ground station. In addition, providing adequate electrical power for these systems is a challenging task because of harsh conditions and high costs of production. Onboard processing addresses these challenges by processing data on-board prior to downlink, instead of storing and forwarding all captured images from onboard sensors to a control station, resulting in the reduction of communication bandwidth and simpler subsequent computations to be performed at ground stations. Therefore, lightweight, small size and low power consumption hardware is essential for onboard real time processing systems. With increasing dimensionality, size and resolution of recent hyperspectral imaging sensors, additional challenges are posed upon remote sensing processing systems and more capable computing architectures are needed. Graphical Processing Units (GPUs) emerged as promising architecture for light weight high performance computing that can address these computational requirements for onboard systems. The goal of this study is to build high performance hyperspectral analysis solutions based on selected high accuracy analysis methods. These solutions are intended to help in the production of complete smart remote sensing systems with low power consumption. We propose accelerated parallel solutions for the well-known recursive hierarchical segmentation (RHSEG) clustering method, using GPUs, hybrid multicore CPU with a GPU and hybrid multi-core CPU/GPU clusters. RHSEG is a method developed by the National 4 Aeronautics and Space Administration (NASA), which is designed to provide more useful classification information with related objects and regions across a hierarchy of output levels. The proposed solutions are built using NVidia’s compute device unified architecture (CUDA) and Microsoft C++ Accelerated Massive Parallelism (C++ AMP) and are tested using NVidia GeForce and Tesla hardware and Amazon Elastic Compute Cluster (EC2). The achieved speedups by parallel solutions compared to CPU sequential implementations are 21x for parallel single GPU and 240x for hybrid multi-node computer clusters with 16 computing nodes. The energy consumption is reduced to 74% using a single GPU compared to the equivalent parallel CPU cluster.