This paper introduces some analysis methods in recent years about imaging spectrometer data at home and abroad. Except the spectral match approach, the methods of the principal componant analysis, the optimal combination of the selected bands, the improved maximum likelihood method, the transformation based on the decision boundary feature matrix, and ortho-subspace projection approach are introduced. Some effects of these methods in application have been described as well.
舒宁. 国内外有关成像光谱数据影像分析方法研究[J]. 国土资源遥感, 1998, 10(1): 16-20.
Shu Ning . A STUDY ON THE IMAGE ANALYSIS METHODS OF IMAGING SPECTROMETER. REMOTE SENSING FOR LAND & RESOURCES, 1998, 10(1): 16-20.
[1] Joseph C Haranyi, et al. Hyperipectral Image Gaasification and Dimemsionality Reduction: An Orthogonal Subspace Projection Approach, IEEE Tns. Geosci. Remote Sensing, 1994, 32(4):[2] Jia Xiuping, et al. Efficient Maximum Likelihood Classification for Imaging Spectrometer Data Sets, IEEE Trans. Geosci.Remote Sensing, 1994, 32(2)[3] Jon Atli Benediktsson, et al, Classification and Feature Extraction of AVIRIS Data, IEEE Trans. Geosci. Remote Sensing,1995,33(5):[4] 舒宁.成像光谱仪影像的几种处理方法.武汉测绘科技大学学报,1997,22(4)