Abstract
Currently, the use of hyperspectral imaging (HSI) for the inspection of microscopic samples is an emerging trend in different fields. The use of push-broom hyperspectral (HS) cameras against other HSI technologies is motivated by their high spectral resolution and their capabilities to exploit spectral ranges beyond 1000 nm. Nevertheless, using push-broom cameras in miscroscopes imposes to perform an accurate spatial scanning of the sample to collect the HS data. In this manuscript, we present a methodology to correctly set-up a push-broom HS microscope to acquire high-quality HS images. Firstly, we describe a custom 3D printed mechanical system developed to perform the spatial scanning by producing a precise linear movement of the microscope stage. Then, we discuss how the dynamic range maximisation, the focusing, the alignment and the adequate speed determination affect the overall quality of the images. Finally, we present some examples of HS data showing the most common defects that usually appear when capturing HS images using a push-broom camera, and also a set of images acquired from real microscopic samples.
Highlights
In the last decades, hyperspectral imaging (HSI) has become a very popular emerging technique employed in numerous areas and applications
We present a customized microscope with HS capabilities as well as a general methodology to correctly couple a HS push-broom camera to a microscope to capture high quality HS images
METHODOLOGY we present a methodology to correctly set up an HS push-broom microscope to capture high quality HS
Summary
Hyperspectral imaging (HSI) has become a very popular emerging technique employed in numerous areas and applications This kind of images collects information along the whole electromagnetic spectrum, covering a wide range of wavelengths which generally span the visible, near-infrared and mid-infrared portions of the spectrum. Absorb or emit electromagnetic energy at specific wavelengths, this characteristic of the HS images permits to reconstruct the radiance spectrum of every image pixel and to identify different materials on the basis of their spectral shape This property makes HS data beneficial for many applications such as vegetation and water resource monitoring [1], non-invasive sensing of food-quality [2], geology [3], diagnosis of multiple cancers [4]–[8], amongothers
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