作　　者： ; ; ;

机构地区： 复旦大学信息科学与工程学院电子工程系

出　　处： 《红外》 2010年第6期23-30,共8页

摘　　要： 传统的光谱混合分析方法假设每个端元必须具有完全稳定的光谱特性,而在实际问题中同类地物的端元光谱往往存在着差异。为了有效地抑制同物异谱对混合像元分解的影响,本文提出一种基于Fisher判别零空间的高光谱遥感图像混合像元分解算法。Fisher判别零空间方法通过对高光谱图像数据进行线性变换,使得变换后的数据中同一端元内的光谱差异减小为零,而不同端元间的光谱差异尽可能地增大。利用变换后的光谱数据对混合像元进行分解就可以较大程度地减少同物异谱现象对分解结果的影响。对模拟高光谱图像数据以及Indiana地区和Cuprite地区的实际AVIRIS数据的解混结果表明,用Fisher判别零空间方法处理混合像元分解问题,可以得到较高的分解精度。 In the traditional spectral mixture analysis method, each endmember is assumed to have a constant spectral signature. However, there are always differences in endmember spectra for same ground objects. In order to restrain the effect of different endmember spectra of the same object on mixed image decomposition, a new method based on Fisher discriminant null space for decomposition of mixed pixels in hyperspectral images is proposed. In the Fisher discriminant null space method, the linear transformation of hyperspectral image data can make the endmember spectra have no variability inside each endmember group and have large differences among different endmember groups. Therefore, the negative impact resulted from the endmember spectral variability can be decreased to a larger extent by using the transformed spectra. The experimental results of both artificial data and actual remote sensing images in Indiana and Cuprite regions show that the proposed algorithm has a higher decomposition accuracy for the mixed pixels in hyperspectral images.

关 键 词： 混合像元分解 同物异谱 判别零空间 高光谱图像

领　　域： [自动化与计算机技术] [自动化与计算机技术]