Particle Swarm Optimization for Hyperspectral Band Selection Using GPU

• Main Authors: Hsu Wang, 王旭
• Other Authors: Yang-Lang Chang
• Format: Others
• Language: zh-TW
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/ynx63z

碩士 === 國立臺北科技大學 === 電機工程系所 === 100 === In recent years, the satellite image technologies have greatly advanced remote sensing community, resulting in the increased number of bands acquired by hyperspectral sensors. The band selection of hyperspectral imagery can reduce the dimensions which can avoid the Hughes phenomena. Therefore, the band selection of hyperspectral imagery has become very important. A band selection algorithm based on particle swarm optimization (PSO) is proposed in this paper. By using the PSO algorithm, the highly correlated bands of hyperspectral imagery can be grouped into the same modules which can extract the most useful information of hyperspectral bands in each module and can further reduce the dimensionality. However the PSO band selection is a time-consuming procedure when the number of hyperspectral bands is huge. Consequently this paper proposes a parallel PSO (PPSO) band selection based on modern graphics processing unit (GPU) architecture using NVIDIA compute unified device architecture (CUDA) technology. It can improve the computational speed of PSO band selection processes. The natural parallelism of proposed PPSO is in the face that each particle can be regarded as an independent agent. Parallel computation benefits the algorithm by providing each agent with one of the parallel processors. The intrinsic parallel characteristics embedded in PPSO can be therefore suitable for a parallel implementation. The effectiveness of the proposed PPSO is evaluated by AVIRIS hyperspectral images. The experimental results demonstrated that the proposed PPSO band selection not only can improve the computational speed but also can offer a satisfactory classification performance.