高光谱数据特征选择与特征提取研究

Study on Feature Selection and Extraction of Hyperspectral Data

高光谱遥感数据的最主要特点是:传统图像维与光谱维信息融合为一体,即“图谱合一”。针对高光谱数据波段多、数据量大、冗余度大等特点,论述了特征选择和特征提取的若干算法,分析了各自的优缺点。重点研究了导数光谱算法,并针对二值编码的不足研究了其改进算法——四值编码算法。最后用编码技术和导数光谱技术提取了地物的光谱特征参数;试验表明:四值编码算法比二值编码算法效果更佳;光谱导数阶数越高,对地物特征的表达越有效。

Because of the character of hyperspectral remote sensing data, it is necessary and urgent to develop hyperspectral data process algorithm. In this paper, some hyperspectral data process algorithms for feature selection and feature extraction was discussed, and its advantage ＆ disadvantage was analyzed. In particular, we studied derivative spectral algorithm and put forward quad-encoding algorithm as the improved the binary encoding algorithm. Using the algorithms this paper proposed we extract spectral absorption parameter. The experiments have demonstrated that quad-encoding algorithm has the better performance than binary encoding on hyperspectral data, and for derivative spectrum it is effective to indicate validate feature for objects when its rank is higher.