基于光谱特征的高光谱遥感影像检索

面向海量遥感信息管理中遥感影像检索的需求,以高光谱遥感信息为例对光谱特征应用进行了探讨,提出基于光谱特征的遥感影像检索包括基于点源示例的检索和基于面源示例的检索,检索中的关键问题是光谱特征提取与相似性度量。基于光谱曲线的相似性度量可以采用光谱角和光谱信息散度进行;基于光谱特征的检索可以通过提取反射和吸收光谱、光谱特征匹配的方法进行;而光谱曲线量化指标如中心矩、分维数和信息熵等的效果较差,不适宜应用于检索。

Spectral Feature-Based Hyperspectral RS Image Retrieval

Oriented to the demands of vast RS information management for RS image (retrieval,) the applications of spectral features are discussed by taking hyperspectral RS image as an example. It is proposed that spectral features-based (retrieval) includes two modes: (retrieval) based on point mask and polygon mask. The most key issues in retrieval are spectral features extraction and similarity (measure.) The spectral vector can be used to retrieval directly, and the spectral angle and spectral information divergence (SID) are (effective) in similarity measure. The local maximum and minimum in reflectance spectral curve, corresponding to reflectance apex and Absorption apex, can be used to retrieval also, but effective matching strategy should be adopted. The quantitative indexes for spectral curves such as moment, fractal and entropy are not suitable to retrieval because of poor similarity measure performance.