高光谱遥感技术具有能同时反映遥感对象空间特征和光谱特征等独特优势, 但这些优势也带来了波段众多且相关性强、数据冗余度高、不利于进一步处理与利用等问题。通过降维可以减少数据中的冗余信息, 提高处理效率, 而特征提取作为降维的一种重要方法, 具有降维速度快等优点。因此, 特征提取对高光谱图像的利用有重要意义。首先介绍了高光谱图像降维的基本原理, 然后对各种高光谱图像特征提取方法进行了分类并归纳总结各自优缺点, 最后指出了高光谱图像特征提取方法研究中存在的问题, 并对高光谱图像特征提取技术发展趋势进行了展望。

Hyperspectral remote sensing technology has the unique advantages of reflecting the spatial and spectral characteristics of remote sensing objects at the same time, but these advantages also bring such problems as too many bands and their strong correlation, high data redundancy, and difficult further processing and utilization.Dimensionality reduction can reduce redundant information in the data and improve the processing efficiency.As an important method of dimensionality reduction, feature extraction has the advantage of fast dimensionality reduction.Therefore, feature extraction is of great significance to the application of hyperspectral images.This paper introduces the basic principle of hyperspectral image dimensionality reduction, classifies and summarizes the advantages and disadvantages of different hyperspectral image feature extraction methods, points out the problems in the research of hyperspectral image feature extraction methods, and puts forward the development trend of hyperspectral image feature extraction technology.