Combined spatial and spectral unmixing of image signals for material recognition in automated inspection systems

In optical inspection systems like automated bulk sorters, hyperspectral images in the near-infrared range are used more and more for identification and classification of materials. However, the possible applications are limited due to the coarse spatial resolution and low frame rate. By adding an additional multispectral image with higher spatial resolution, the missing spatial information can be acquired. In this paper, a method is proposed to fuse the hyperspectral and multispectral images by jointly unmixing the image signals. To this end, the linear mixing model, which is well-known from remote sensing applications, is extended to describe the spatial mixing of signals originating from different locations. Different spectral unmixing algorithms can be used to solve the problem. The bene fit of the additional sensor and the properties of the unmixing process are presented and evaluated, as well as the quality of unmixing results obtained with different algorithms. With the proposed extended mixing model, an improved result can be achieved, as shown with different examples.