1. 北京理工大学光电学院, 北京 100081 2. 光电成像技术与系统教育部重点实验室，北京理工大学，北京 100081 3. Polytechnic Institute of New York University, Brooklyn, NY, U.S. 11201
Research on Spectral Data Feature Extraction Based on Wavelet Decomposition
CHEN Gang1，3, CHEN Xiao-mei1，2*, LI Ting1，2, NI Guo-qiang1，2
1. School of Optoelectronics, Beijing Institute of Technology, Beijing 100081, China 2. Key Laboratory of Photoelectronic Imaging Technology and System，Beijing Institute of Technology, Ministry of Education of China, Beijing 100081, China 3. Polytechnic Institute of New York University, Brooklyn, NY, U.S. 11201
Abstract：Reflectance spectral curve reveals the unique physical characteristic of different materials. Through spectral match and recognition, different materials could be distinguished. Because of the great amount of spectral data and the ambiguous absorption feature of original spectral curve, feature extraction of reflectance spectral curve is one of the essential techniques in hyperspectral image classification and recognition. Using wavelet decomposition technique, the present paper proposes a new spectral feature extraction algorithm to compress data amount while reserve spectral feature selectively. Through selecting the appropriate decomposition level by measuring the objective absorption feature frequency, the original signal would be projected into a new feature space with less data amount and more obvious objective feature than the original one. The experiments show that the method proposed can reduce the spectrum dimensions effectively and improve the recognition precision with the spectrum matching.
陈 刚1,3，陈小梅1,2*，李 婷1,2，倪国强1,2 . 基于小波分解的光谱特征提取算法研究 [J]. 光谱学与光谱分析, 2010, 30(11): 3027-3030.
CHEN Gang1，3, CHEN Xiao-mei1，2*, LI Ting1，2, NI Guo-qiang1，2 . Research on Spectral Data Feature Extraction Based on Wavelet Decomposition . SPECTROSCOPY AND SPECTRAL ANALYSIS, 2010, 30(11): 3027-3030.
