Spectral and spatial unmixing for materialrecognition in sorting plants

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. Therefore, the linear mixing
model, which is well-known from remote sensing applications,
is extended to describe the spatial mixing of signals originated
from different locations. Different spectral unmixing algorithms
can be used to solve the problem. The benefit of the additional
sensor and the unmixing process is 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.