GPU-Accelerated K-Means Image Clustering

• Main Authors: Chao-Wei Chiu, 邱兆偉
• Other Authors: Victor J. D. Tsai
• Format: Others
• Language: zh-TW
• Published: 2014
• Online Access: http://ndltd.ncl.edu.tw/handle/02981949068777197122

碩士 === 國立中興大學 === 土木工程學系所 === 102 === K-Means clustering has been a widely used approach in unsupervised classification of remotely sensed images. Due to recent emerging development in Graphics Processing Units (GPUs), the computing performance and memory bandwidth of GPUs have been much higher than those of Central Processing Units (CPUs). Therefore, it is expected to accelerate K-Means clustering by parallel computing in GPUs. This research aims on developing a GPU-optimized parallel processing approach for fast unsupervised classification of remotely sensed images using C++ and NVIDIA’s CUDA. The basic idea of traditional K-Means approach was refined with minimum distance classifier in this research for clustering images. The performance of numerical experiments in clustering 3-band color aerial images, in the size of 1360×1020 and scale-down 680×510, into specified number of spectral clusters will be demonstrated for the advantages of 10 to 20 speed-up ratio in computational efficiency of the GPU-based approach in a highly parallel, multi-thread, and multi-core implementation against traditional CPU-based approach.