[The analysis of error sources for SAM and its improvement algorithms].
Based on the analysis of the error sources for spectral anglemapping (SAM), several key elements are pointed out, i.e. the change of wave band location, the change of the attribution ratio, the random change of attribution, and the whole translation of wave band. After the above-mentioned four error sources are analyzed, the authors present several improvement algorithms, viz. calculating the spectral angle with grouping, normalization and intersection. The grouping method can resolve the pseudo-similar problem, because it considers both spectral global features and local features. Calculating spectral angle with normalization restrains those random errors in original data by normalizing the spectral vectors. The intersection method can eliminate the error elicited by the whole wave translation. Therefore, it can be employed to correctly identify spectral class. Experiments show that those improvement algorithms are effective and can be used to process spectral data with errors.
Tang
Hong
H
The Institute of Image Processing and Pattern Recognition, Shanghai Jiaotong University, Shanghai 200030, China.
Du
Pei-jun
PJ
Fang
Tao
T
Shi
Peng-fei
PF
D000465:Algorithms; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D012015:Reference Standards; D015203:Reproducibility of Results; D013057:Spectrum Analysis / Q000379:methods* / Q000592:standards