Error analysis and improvements of spectral angle mapper (SAM) model

Peijun Du; Yunhao Chen; Tao Fang; Hong Tang

doi:10.1117/12.654850

3 November 2005 Error analysis and improvements of spectral angle mapper (SAM) model

Peijun Du, Yunhao Chen, Tao Fang, Hong Tang

Proceedings Volume 6043, MIPPR 2005: SAR and Multispectral Image Processing; 60430L (2005) https://doi.org/10.1117/12.654850
Event: MIPPR 2005 SAR and Multispectral Image Processing, 2005, Wuhan, China

Abstract

Spectral Angle Mapper (SAM) model has got wide applications in hyperspectral Remote Sensing (RS) information processing. But Spectral Angle couldn't achieve satisfied performance in some cases because of its sensitivity to noises and uncertainty. Based on the analysis to traditional SAM algorithm, four types of errors and their impacts to spectral angle are investigated. In order to reduce the impacts of above errors, some improved algorithms are proposed and experimented. The first improved algorithm is grouping spectral angle algorithm. In this new algorithm all bands are divided into two sets by odd and even bands, that means two additional sub-vectors are created in addition to the original spectral vector. So three spectral angles will be computed and the minimum of three indexes is used as final index. The second improved algorithm is normalized spectral angle. In this way spectral angle is computed to the normalized vectors of two original vectors. Two approaches are used to normalize the spectral vector, and spectral angle is computed to the normalized vectors. This algorithm is able to decrease the impacts of random errors. The third algorithm is intersected spectral angle. Spectral angle is calculated by a spectral displacement strategy in this approach. That means a given displacement to change the corresponding bands of two spectral vectors is used and a spectral angle to the displaced vectors will be got. By this displacement strategy the impacts of band offset is reduced. Finally some experiments are used to test those improved algorithms. It proves that those new approaches can reduce and control the errors and improve the precision and reliability of similarity measure.

Citation Download Citation

Peijun Du, Yunhao Chen, Tao Fang, and Hong Tang "Error analysis and improvements of spectral angle mapper (SAM) model", Proc. SPIE 6043, MIPPR 2005: SAR and Multispectral Image Processing, 60430L (3 November 2005); https://doi.org/10.1117/12.654850

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