A hyperspectral beam-scanning microscope operating in the long wave infrared (LWIR) is demonstrated for future application to stand-off imaging platforms. A 32-channel quantum-cascade laser (QCL) array enables rapid wavelength modulation for fast hyperspectral imaging through sparse sampling in position and wavelength, which when coupled with image reconstruction techniques can enhance frame rate. Initial measurements of dichloromethane and water mixtures are shown, utilizing spectral information for classification across the field of view. Ongoing efforts aim to utilize copropagating visible and IR beams to enhance spatial resolution for the IR measurements by combining spatial information retrieved from visible images obtained concurrently. Future work will leverage Lissajous trajectories for sparsely-sampled beam-scanning and extend the image interpolation algorithms to arbitrary dimension for sparse sampling in the spectral domain. Simulations of the error associated with various sparse-sampling methods are also presented herein which support the use of Lissajous trajectories as a sparse-sampling method in beam-scanning microscopy.
Raman spectra were perturbed such that an intentional misclassification was induced when using a dimension reduction classifier such as linear discriminate analysis (LDA). These perturbations were primarily targeted at patterning the noise within the spectra such that detection is difficult to detect by visual inspection. Data-intensive decisions are increasingly important to mine the increasing volume of information accessible by modern instrumentation. These decisions are conceptually performed through projection of measurements on high dimensional manifolds to low-dimensional outcomes. This dimension reduction provides suppression of stochastic random noise to better inform the decision. However, non-stochastic patterning of the “noise” can induce intentional misclassification that is difficult to easily detect by visual inspection. Such digital attacks could result in intentional changes in decisions made from many routine automated classifiers. Preliminary results using Raman spectra showed that misclassification can be induced by picking a target classification and patterning the noise in the spectra such that in a reduced dimensional space, it is moved towards the target classification. Development of approaches for optimizing the attacks serves as a prelude for generation of robust classification strategies less susceptible to intentional attacks.