Wood Species Classification in Open Set Using an Improved NNO Classifier

Abstract

A wood species classification scheme was developed based on open set using an improved Nearest Non-Outlier (NNO) classifier. Near infrared (NIR) spectral curves were collected in spectral band 950 to 1650 nm by a micro spectrometer. The spectral dimension reduction was performed with a Metric Learning (ML) algorithm. Two improvements were proposed in the following NNO classifier. First, a cluster analysis was performed in each wood class by using a Density Peak Clustering (DPC) algorithm to get 1 to 3 clusters. A fixed threshold  for all wood classes was replaced by a variable  for all clusters. This threshold  defines an internal boundary for one wood species to further compute a class membership score for all wood species. The classification accuracy based on these clusters of each wood class was better than that based on each class. The experimental results in different open set scenarios demonstrate that the improved NNO classifier outperformed the original NNO classifier and some other state-of-the-art open set recognition (OSR) algorithms.