Hyperspectral Video for Forensic Applications

Abstract

Hyperspectral Imaging is a technique where material properties and quantities are determined using interactions between light and matter. Its non-contact and non-destructive nature ensures it has numerous applications in forensic science. Many of these applications require a video-rate hyperspectral system, although, processing this volume of data is demanding and difficult to perform in real-time. A novel method is presented for reducing the complexity of current hyperspectral imaging techniques in the context of a hyperspectral video crime scene analysis tool. Specifically, the essential but time-consuming phase of dimension reduction is achieved using a new on-line estimate of the principal components and exploits temporal redundancy in sequential hyperspectral volumes. This new algorithm is shown to provide a significant reduction in complexity (> 10× ) in processing hyperspectral video when coupled with Abundance Guided Endmember Selection—a new endmember identification and extraction algorithm developed for hyperspectral video applications. A theoretical frame-rate of over 20fps for a scene with 5×10⁶ pixels and 224 bands can be achieved when implemented on an nVidia Tesla C2070.