Miniaturized hyperspectral imaging sensors are becoming available to small unmanned airborne vehicle (UAV) platforms. Imaging concepts based on frame format offer an attractive alternative to conventional hyperspectral pushbroom scanners because they enable enhanced processing and interpretation potential by allowing for acquisition of the 3-D geometry of the object and multiple object views together with the hyperspectral reflectance signatures. The objective of this investigation was to study the performance of novel visible and near-infrared (VNIR) and short-wave infrared (SWIR) hyperspectral frame cameras based on a tunable Fabry-Pérot interferometer (FPI) in measuring a 3-D digital surface model and the surface moisture of a peat production area. UAV image blocks were captured with ground sample distances (GSDs) of 15, 9.5, and 2.5 cm with the SWIR, VNIR, and consumer RGB cameras, respectively. Georeferencing showed consistent behavior, with accuracy levels better than GSD for the FPI cameras. The best accuracy in moisture estimation was obtained when using the reflectance difference of the SWIR band at 1246 nm and of the VNIR band at 859 nm, which gave a root mean square error (rmse) of 5.21 pp (pp is the mass fraction in percentage points) and a normalized rmse of 7.61%. The results are encouraging, indicating that UAV-based remote sensing could significantly improve the efficiency and environmental safety aspects of peat production.

Spectral cameras are traditionally used in remote sensing of microalgae, but increasingly also in laboratory-scale applications, to study and monitor algae biomass in cultures. Practical and cost-efficient protocols for collecting and analyzing hyperspectral data are currently needed. The purpose of this study was to test a commercial, easy-to-use hyperspectral camera to monitor the growth of different algae strains in liquid samples. Indices calculated from wavebands from transmission imaging were compared against algae abundance and wet biomass obtained from an electronic cell counter, chlorophyll a concentration, and chlorophyll fluorescence. A ratio of selected wavebands containing near-infrared and red turned out to be a powerful index because it was simple to calculate and interpret, yet it yielded strong correlations to abundances strain-specifically (0.85 < r < 0.96, p < 0.001). When all the indices formulated as A/B, A/(A + B) or (A − B)/(A + B), where A and B were wavebands of the spectral camera, were scrutinized, good correlations were found amongst them for biomass of each strain (0.66 < r < 0.98, p < 0.001). Comparison of near-infrared/red index to chlorophyll a concentration demonstrated that small-celled strains had higher chlorophyll absorbance compared to strains with larger cells. The comparison of spectral imaging to chlorophyll fluorescence was done for one strain of green algae and yielded strong correlations (near-infrared/red, r = 0.97, p < 0.001). Consequently, we described a simple imaging setup and information extraction based on vegetation indices that could be used to monitor algae cultures.

Commercial hyperspectral imagers (HSIs) are expensive and thus unobtainable for large audiences or research groups with low funding. In this study, we used an existing do-it-yourself push-broom HSI design for which we provide software to correct for spectral smile aberration without using an optical laboratory. The software also corrects an aberration which we call tilt. The tilt is specific for the particular imager design used, but correcting it may be beneficial for other similar devices. The tilt and spectral smile were reduced to zero in terms of used metrics. The software artifact is available as an open-source Github repository. We also present improved casing for the imager design, and, for those readers interested in building their own HSI, we provide print-ready and modifiable versions of the 3D-models required in manufacturing the imager. To our best knowledge, solving the spectral smile correction problem without an optical laboratory has not been previously reported. This study re-solved the problem with simpler and cheaper tools than those commonly utilized. We hope that this study will promote easier access to hyperspectral imaging for all audiences regardless of their financial status and availability of an optical laboratory.

Python is a very popular programming language among data scientists around the world. Python can also be used in hyperspectral data analysis. There are some toolboxes designed for spectral imaging, such as Spectral Python and HyperSpy, but there is a need for analysis pipeline, which is easy to use and agile for different solutions. We propose a Python pipeline which is built on packages xarray, Holoviews and scikit-learn. We have developed some of own tools, MaskAccessor, VisualisorAccessor and a spectral index library. They also fulfill our goal of easy and agile data processing. In this paper we will present our processing pipeline and demonstrate it in practice.

In many cases, chemotherapeutic anticancer therapies’ contribution to life extension is low, and serious adverse effects are common.^1,2 Also the cost of cancer treatments is extremely high and is increasing constantly.²

The Fabry-Perot interferometers (FPI) are essential components of many hyperspectral imagers (HSI). While the Piezo-FPI (PFPI) are still very relevant in low volume, high performance applications, the tunable MOEMS FPI (MFPI) technology enables volume-scalable manufacturing, thus having potential to be a major game changer with the advantages of low costs and miniaturization. However, before a FPI can be utilized, it must be integrated with matching optical assembly, driving electronics and imaging sensor. Most importantly, the whole HSI system must be calibrated to account for wide variety of unwanted physical and environmental effects, that significantly influence quality of hyperspectral data. Another challenge of hyperspectral imaging is the applicability of produced raw data. Typically it is relatively low and an application specific software is necessary to turn data into meaningful information. A versatile analysis tools can help to breach the gap between raw hyperspectral data and the user application. This paper presents a novel HSI hardware platform that is compatible with both MFPI and PFPI technologies. With an MFPI installed, the new imager can have operating range of λ = 600 - 1000 nm with FWHM of 15 - 25 nm and tuning speed of < 2 ms. Similar to previous imager in Ref. 1, the new integrated HSI system is well suited for mobile and cloud based applications due to its small dimensions and connectivity options. In addition to new hardware platform, a new hyperspectral imaging analysis software was developed. The new software used in conjunction with the HSI provides a platform for spectral data acquisition and a versatile analysis tool for a processing raw data into more meaningful information.