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 benefit 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.

Date Published: 24 May 2013
PDF: 12 pages
Proc. SPIE 8791, Videometrics, Range Imaging, and Applications XII; and Automated Visual Inspection, 87911E (24 May 2013); doi: 10.1117/12.2021660

Published in SPIE Proceedings Vol. 8791:
Videometrics, Range Imaging, and Applications XII; and Automated Visual Inspection
Fabio Remondino; Jürgen Beyerer; Fernando Puente León; Mark R. Shortis, Editor(s)