Hyperspectral Wavelength Selection and Integration for Bruise Detection of Korla Pears

Abstract

Wavelength selection is a challenging job for the detection of the bruises on pears using hyperspectral imaging. Most modern research used the feature wavelength set selected by a single selection method which is generally unable to handle the wide variability of the hyperspectral data. A novel framework was proposed in this work to increase the performance of the bruise detection, through combining three state-of-the-art variable selection methods and the concept of feature-level integration. Successive projection algorithm, competitive adaptive reweighted sampling, and RELIEF were first applied to the spectra of the Korla pear, respectively. Then, the corresponding feature wavelength subsets were integrated and an optimal feature wavelength set was constructed. An ELM-based classifier was employed for the pear bruise identification finally. Experimental results demonstrated that the feature wavelength integration resulted in lower detection errors. The proposed method is simple and promising for bruise detection of Korla pears, and it can be utilized for other types of defects on fruits.