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[陆地物体的光谱不确定性及光谱角映射器算法的适用性]

[Spectral Uncertainty of Terrestrial Objects and the Applicability of Spectral Angle Mapper Algorithm].

作者信息

Cen Yi, Zhang Gen-zhong, Zhang Li-fu, Lu Xu-hui, Zhang Fei-zhou

出版信息

Guang Pu Xue Yu Guang Pu Fen Xi. 2015 Oct;35(10):2841-5.

Abstract

The spectral uncertainty of terrestrial objects causes a certain degree of spectral differences among feature spectra, which affects the accuracy of object recognition and also impacts the object recognition of spectral angle mapper algorithm (SAM). The spectral angle mapper algorithm is based on the overall similarity of the spectral curves, which was widely used in the classification of hyperspectral remotely sensed information. The spectral angle mapper algorithm does not take the spectral uncertainty of terrestrial objects into account while calculating the spectral angle between the spectral curves, and therefore does not tend to correctly identify the target objects. The applicability of the spectral angle mapper algorithm is studied for the spectral uncertainty of terrestrial objects and a modified SAM is proposed in this paper. In order to overcome the influence of the spectral uncertainty, the basic idea is to set a spectral difference value for the test spectra and the reference spectra and to calculate the spectral difference value based on derivation method according to the principle of minimum angle between the test spectra and the reference spectra. By considering the impact of the spectral uncertainty of terrestrial objects, this paper uses five kaolinite mineral spectra of USGS to calculate the spectral angle between the five kalinite mineral spectra by using local band combination and all bands to verify the improved algorithm. The calculation results and the applicability of the spectral angle mapper algorithm were analyzed. The results obtained from the experiments based on USGS mineral spectral data indicate that the modified SAM is not only helpful in characterizing and overcoming the impact of the spectral uncertainty but it can also improve the accuracy of object recognition to certain extent especially for selecting local band combination and has better applicability for the spectral uncertainty of terrestrial objects.

摘要

地面物体的光谱不确定性导致特征光谱之间存在一定程度的光谱差异,这影响了目标识别的准确性,也对光谱角映射器算法(SAM)的目标识别产生影响。光谱角映射器算法基于光谱曲线的整体相似性,广泛应用于高光谱遥感信息分类。该算法在计算光谱曲线之间的光谱角时未考虑地面物体的光谱不确定性,因此难以正确识别目标物体。本文针对地面物体的光谱不确定性研究了光谱角映射器算法的适用性,并提出了一种改进的SAM。为克服光谱不确定性的影响,基本思路是为测试光谱和参考光谱设置一个光谱差值,并根据测试光谱与参考光谱之间最小角度的原理,采用导数方法计算光谱差值。考虑到地面物体光谱不确定性的影响,本文利用美国地质调查局(USGS)的五种高岭土矿物光谱,通过局部波段组合和全波段计算这五种高岭土矿物光谱之间的光谱角,以验证改进算法。分析了光谱角映射器算法的计算结果及其适用性。基于USGS矿物光谱数据的实验结果表明,改进后的SAM不仅有助于表征和克服光谱不确定性的影响,还能在一定程度上提高目标识别的准确性,特别是在选择局部波段组合时,对地面物体的光谱不确定性具有更好的适用性。

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