The EEMD method of adaptive standard deviation noise is applied to hyperspectral image classification on GPU-base

• Main Authors: HSIEH,CHIA-LIN, 謝佳霖
• Other Authors: 張陽郎
• Format: Others
• Language: zh-TW
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/k9kx57

碩士 === 國立臺北科技大學 === 電機工程系所 === 105 === The maturity of telemetry technology is widely used in various fields，Such as environmental assessment, geological mapping, agricultural / forestry applications, water resources and urban planning applications. This study uses passive optical telemetry - hyperspectral images , Non-visible light under different substances in different wavelengths will produce a different absorption and reflection, You can observe these different spectral signals by the nature of the material itself to produce a unique spectral curve And then in a large database to do a variety of material classification. How to quickly and correctly classify the minerals in the huge database of hyperspectral signal is this paper focuses on content, Spectral Angle Mapping and Euclidean distance, as opposed to the common spectral similarity comparison method,this paper uses (Ensemble Empirical Mode Decomposition, EEMD)in hyperspectral analysis. The hyperspectral signal is a non-linear and non-stationary signal with a good match between EEMD, But there are two problems that need to be solved using EEMD operationsOne for the operation process need to add white noise (White noise), how to determine his weighting? In the past, the weighting factor of the noise is brought by experience but this parameter is not the best parameter value, Another problem is that EEMDs computational process takes time,In the need to deal with large amounts of information often need a lot of time,How to solve the above two points for the focus of this paper.The experimental results show that the intensity of the white noise applied in the decomposition of EEMD is calculated by the standard deviation of each signal itself. It is possible to achieve better elasticity by setting a fixed parameter value in order to solve the difference between different signals , And save a lot of time to find parameters, the use of parallel computing GPU is an effective solution to the problem of too large to achieve more accurate and faster material classification.