Brillouin imaging relies on the reliable extraction of subtle spectral information from hyperspectral datasets. To date, the mainstream practice has been to use line fitting of spectral features to retrieve the average peak shift and linewidth parameters. Good results, however, depend heavily on sufficient signal-to-noise ratio and may not be applicable in complex samples that consist of spectral mixtures. In this work, we thus propose the use of various multivariate algorithms that can be used to perform supervised or unsupervised analysis of the hyperspectral data, with which we explore advanced image analysis applications, namely unmixing, classification and segmentation in a phantom and live cells.The resulting images are shown to provide more contrast and detail, and obtained on a timescale $10 2 faster than fitting. The estimated spectral parameters are consistent with those calculated from pure fitting.

In a complex urban environment, images acquired with very high spatial resolution are often plagued with shadows. Shadows distort the spectral features of materials, possibly crippling many image-based applications such as visualization, anomaly detection and classification. Moreover, it is very difficult to perform accurate atmospheric compensation when in shadow due to the complex interaction of radiative components. This paper proposes a novel empirical atmospheric compensation method that is applicable to both sunlit and shadowed regions, and does not require the use of 3-D geometrical data nor Digital Elevation Models (DEM). The resulting reflectance images were then tested for the purposes of visualization, anomaly detection and classification.

Polarimetric imaging can provide valuable information about biological samples in a wide range of applications. Detrimental tissue scattering and depolarization however currently hamper in vivo polarization imaging. In this work, single pixel imaging is investigated as a means of reconstructing polarimetric images through scattering media. A theoretical imaging model is presented, and the recovery of the spatially resolved Mueller matrix of a test object behind a scattering phantom is demonstrated experimentally.

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