| Summary: | 碩士 === 國立臺北科技大學 === 電機工程系研究所 === 100 === Recently, the information of ground surface has been recode with Hyperspectral device massively and recognition the ground material by analysis the spectral data. The k-Nearest Neighbor(k-NN) algorithm is widely used in classify, the main idea of k-NN algorithm is that find the k nearest neighbor and voting by their class ID. However, the overlapping of different training sample groups will cause false classification. For overcome this problem, we trying to use Nearest Feature Space(NFS) algorithm to keep the structure of training samples and calculate the nearest distance between test sample and the feature space of training samples.
Although, NFS can get a better correctness rate of classification, it will spend a huge time when the training sample is too much. For this reason, we propose a parallelism method of NFS algorithm based on training samples, distributing different feature space to corresponding core of GPUs thought Compute Unified Device Architecture(CUDA). For reduce the transform delay between Host and Device, adapting data between different memories in GPU carefully is needed.
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