高光谱遥感信息中的特征提取与应用研究

特征提取、度量与分析是高光谱遥感应用的基础.面向高光谱遥感数据的特点, 将光谱特征划分为点尺度、面尺度和体尺度三个尺度的特征.基于特征属性与算法原理, 构建了光谱曲线特征、光谱变换特征和光谱度量特征三个层次的高光谱遥感光谱特征体系, 并对光谱特征提取与应用进行了深入探讨.光谱曲线特征包括直接光谱编码、光谱反射与吸收特征等, 光谱变换特征包括植被指数、导数光谱等, 光谱度量特征则包括光谱角、SID、相关系数和距离等.在分析特征算法原理的同时对其特点和应用进行了探讨.试验表明四值编码、光谱角和SID在应用中能够取得较好的效果.

Spectral Features Extraction in Hyperspectral RS Data and Its Application to Information Processing

Oriented to the demands of hyperspectral RS information processing and applications, spectral features in hyperspectral RS image can be categorized into three scales: point scale, block scale and volume scale. Based on the properties and algorithms of different features, it is proposed that point scale features can be divided into three levels: spectral curve features, spectral transformation features and spectral similarity measure features. Spectral curve features include direct spectra encoding, reflection and absorption features. Spectral transformation features include Normalized Difference of Vegetation Index (NDVI), derivate spectra and other spectral computation features. Spectral similarity measure features include spectral angle (SA), Spectral Information Divergence(SID), spectral distance, correlation coefficient and so on. Based on analysis to those algorithms, several problems about feature extraction, matching and application are discussed further, and it proved that quaternary encoding, spectral angle and SID can be used to information processing effectively.