The operation of hyperspectral imaging systems in industrial environments can be a challenge. In the nuclear industry, partially transparent elements such as gloveboxes or panels are often used to cover samples for protection against the risk of contamination. In practical terms, this means that the hyperspectral sensors can only capture data through partially transparent media, which interferes the vision between sensor and sample. Representative examples of these media are Polymethyl Methacrylate (PMMA) or acrylic and Polycarbonate (PC). In this work, we evaluate the effect that the transparent media can have on the data when captured under these conditions, where transparent materials are placed between sensor and sample. Experiments include hyperspectral images of the same samples captured with and without panel obstruction for a direct comparison of spectral responses, suggesting potential artificial intelligence techniques and methods to identify these effects and mitigate them.
This work reviews and presents a comparison of hyperspectral imaging results when analyzing corrosion products in the ultraviolet (UV) range (250 nm to 500 nm), visible near-infrared (VNIR) range (400 to1000 nm) and shortwave-infrared range (900 to 2500 nm). In related and prior work in our group, corrosion products on steel have been detected using hyperspectral imaging in the VIS, NIR and SWIR regions of the spectrum. However, an extensive review of the academic literature has revealed that the hyperspectral response of corrosion in the UV has not been reported. To address this, we present our results of imaging corrosion products on metal substrates using our Headwall UV-VIS Hyperspectral imaging sensor. These results are contrasted with the same samples imaged using our Headwall VNIR E series and Headwall SWIR 640 Hyperspectral imaging sensors. Our initial results indicate that corrosion spectra in the UV are separable from those of steel, but that the VNIR is the most appropriate range for this type of determination.
The dual requirement for high spatial and substance specificity makes stand-off in-theatre biological detection of surface biological contaminants extremely challenging. We will describe a novel combined fluorescence multispectral imaging (MSI) and stand-off Raman approach which are united through their use of deep-UV (sub-250 nm excitation. This allows high-confidence location and classification of candidate contamination sites over the camera field of view, and subsequent resonance-Raman classification of these identified sites. Stand-off Raman is enabled through the use of a novel, extremely high-throughput Spatial Heterodyne spectrometer. The viability of this approach is confirmed through its use on application relevant biological simulant samples.
High resolution aerial and satellite borne hyperspectral imagery provides a wealth of information about an imaged scene allowing for many earth observation applications to be investigated. Such applications include geological exploration, soil characterisation, land usage, change monitoring as well as military applications such as anomaly and target detection. While this sheer volume of data provides an invaluable resource, with it comes the curse of dimensionality and the necessity for smart processing techniques as analysing this large quantity of data can be a lengthy and problematic task. In order to aid this analysis dimensionality reduction techniques can be employed to simplify the task by reducing the volume of data and describing it (or most of it) in an alternate way. This work aims to apply this notion of dimensionality reduction based hyperspectral analysis to target detection using a multivariate Percentage Occupancy Hit or Miss Transform that detects objects based on their size shape and spectral properties. We also investigate the effects of noise and distortion and how incorporating these factors in the design of necessary structuring elements allows for a more accurate representation of the desired targets and therefore a more accurate detection. We also compare our method with various other common Target Detection and Anomaly Detection techniques.
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