Impact of class noise on performance of hyperspectral band selection based on neighborhood rough set theory

Hyperspectral imaging has the ability for characteristic identification because of its rich physical and chemical information. To develop online or portable devices for industrial applications, informative spectral bands relevant to the specific objects are needed to be selected. The neighborhood rough set (NRS) theory is an effective tool for selecting bands. The hyperspectral datasets gathered from practical applications often contain noise. The performance of band selection algorithm is adversely influenced by noise. In this paper, the performance of the band selection algorithm in the presence of class noise was assessed, which based on consistency measure, dependency measure and information measure of the NRS theory. The robustness and classification performance of algorithms for soybean and maize classification using hyperspectral imaging were compared systematically under different levels of noise. The results demonstrate that the robustness and classifying ability of all algorithms decreases in general when the number of mislabeled samples increases in training dataset and testing datasets. Compared with other algorithms based on the NRS theory and some classic algorithms (uninformative variable elimination and successive projections algorithm), the variable precision neighborhood rough set algorithm is proven to be superior. It is more robust and more accurate when class noise occurs in training dataset or testing datasets. This research not only highlights the strong and weak characteristics of the different approaches for different levels of noise, but also provides effective solutions in handling class noise.