Spectral Features Extraction in Hyperspectral RS Data and Its Application to Information Processing
高光谱遥感信息中的特征提取与应用研究

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.