Volume III-7
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-7, 97–102, 2016
https://doi.org/10.5194/isprs-annals-III-7-97-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-7, 97–102, 2016
https://doi.org/10.5194/isprs-annals-III-7-97-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

  07 Jun 2016

07 Jun 2016

INVESTIGATION OF LATENT TRACES USING INFRARED REFLECTANCE HYPERSPECTRAL IMAGING

Till Schubert, Susanne Wenzel, Ribana Roscher, and Cyrill Stachniss Till Schubert et al.
  • Department of Photogrammetry, Institute of Geodesy and Geoinformation, University of Bonn, Germany

Keywords: Hyperspectral Imaging, Forensics, Infrared Spectroscopy, Classification, Random Forest, Markov Random Fields

Abstract. The detection of traces is a main task of forensics. Hyperspectral imaging is a potential method from which we expect to capture more fluorescence effects than with common forensic light sources. This paper shows that the use of hyperspectral imaging is suited for the analysis of latent traces and extends the classical concept to the conservation of the crime scene for retrospective laboratory analysis. We examine specimen of blood, semen and saliva traces in several dilution steps, prepared on cardboard substrate. As our key result we successfully make latent traces visible up to dilution factor of 1:8000. We can attribute most of the detectability to interference of electromagnetic light with the water content of the traces in the shortwave infrared region of the spectrum. In a classification task we use several dimensionality reduction methods (PCA and LDA) in combination with a Maximum Likelihood classifier, assuming normally distributed data. Further, we use Random Forest as a competitive approach. The classifiers retrieve the exact positions of labelled trace preparation up to highest dilution and determine posterior probabilities. By modelling the classification task with a Markov Random Field we are able to integrate prior information about the spatial relation of neighboured pixel labels.