A New Hyperspectral Redundant Band Detection Method Based on Local Hurst Exponent

Abstract

Hyperspectrum reflectance is a curve in a certain wavelength range. Its complex dynamic structure reflects rich information of the object at variable bands. However, the potential redundancy will seriously affect accurate extraction of spectral features, therefore, information redundancy detection is a critical pretreatment for spectral analysis. In this paper, by using the local detrended fluctuation analysis, we propose a new method to detect the redundant bands. The method focuses on the spectral auto-correlation represented by local Hurst exponent in moving windows. Thus, the redundant band can be determined by the comparison of auto-correlation between two adjacent windows. To test our method, using the fractal feature of the removing redundant bands as augment, rapeseed oleic acid's prediction model is constructed based on random decision forest method. As comparison, the same feature of the original spectrum is also employed as augment for the model. The result shows that the feature of removing the redundant bands will bring better model performance than the feature of original spectrum does.