摘要
物质的光谱曲线反映了其独特的反射特性,利用光谱可以进行物质的分类识别工作。由于光谱曲线数据量较大、吸收特征不明显等特点,光谱曲线的特征提取是高光谱影像分类识别中的一个关键问题之一。该文利用小波分析技术通过对原始信号的分解,以及测量目标光谱特征的吸收宽度,确定了小波分解尺度,达到突出目标光谱吸收特征而抑制非相关特征及噪声的目的。通过实验表明,该方法可有效地降光谱维数,提高了光谱匹配识别精度。
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.
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2010年第11期3027-3030,共4页
Spectroscopy and Spectral Analysis
基金
国家高技术研究发展计划(863计划)项目(2008AA121103)资助
关键词
光谱分析
特征提取
小波分解
Spectral analysis
Feature extraction
Wavelet decomposition
