摘要
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。
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.
出处
《光谱学与光谱分析》
SCIE
EI
CAS
CSCD
北大核心
2005年第8期1171-1175,共5页
Spectroscopy and Spectral Analysis
基金
国家"863"高技术基础(2001AA135091)
中国博士后科学基金(2002032152)
山东省基础地理信息与数字化技术重点实验室开放基金(2002-03)资助项目
关键词
基于内容的图像检索
高光谱遥感影像
光谱特征
相似性度量
Content-based image retrieval （CBIR）
Hyperspectral remote sensing image
Spectral feature
Similarity measure
