Comparison of VIS/NIR spectral curves plus RGB images with hyperspectral images for the identification of Pterocarpus species

Abstract The image information and spectral information of wood sections can be used to identify wood species. Hyperspectral images have both image information and spectral information, but they have disadvantages such as large data capacity, slow reading speed, and the necessity of expensive equipment for their acquisition. In this study, the classification results of Pterocarpus by using visible/near infrared (VIS/NIR) spectral information and RGB images were compared with hyperspectral images. The VIS/NIR spectral curves, Hyperspectral, and RGB images of five wood species of Pterocarpus with similar transverse-sections were collected. In feature-level fusion, the feature vectors are directly connected in series, and features fused by canonical correlation analysis are compared. In decision-level fusion, an extreme learning machine and a composite-kernel support vector machine (SVM) are used and compared. In the feature- and decision-level fusion methods, the recognition results of VIS/NIR spectral curves plus RGB images were largely similar to those of hyperspectral images. Therefore, a recognition effect similar to that of the hyperspectral image can be obtained by collecting the spectral information and image information of wood sections separately, which can reduce the cost of data acquisition and improve the speed of data processing.