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.

The design of photobioreactors for microalgae cultivation aims to achieve an architecture that allows the most efficient photosynthetic growth. The availability of light at wavelengths that are important for photosynthesis is therefore particularly crucial for reactor design. While testing different reactor types in practice is expensive, simulations could effectively limit the range of material and reactor design options. In this study, procedural three-dimensional modelling together with ray tracing was used to create virtual models of a conventional glass photobioreactor lit from the outside and a steel photobioreactor with embedded light sources. The measured transmittance and reflectance of Chlorella vulgaris culture were used as a basis for light interaction simulation, and spectral images of the same species were used to validate the simulation results. This type of simulation could have the potential for comparing different reactor architectures, geometries and light attenuation to facilitate the transition to large-scale cultivation. Our results show that the proposed simulator is usable in photobioreactor geometry design as well as in the estimation of available illumination on wavelengths where microalgae have strong absorption peaks, but the handling of light scattering still needs improvement. To the authors’ best knowledge, this is the first attempt, not focused on a specific use case, to build a general photobioreactor design tool capable of estimating hyperspectral light attenuation in microalgae suspension. All software code and used datasets are made available for the reader as open source.

In recent decades, remote sensing of vegetation by hyperspectral imaging has been of great interest. An important part in interpreting the remotely sensed spectral data is played by simulators, which approximate the connection between plants’ biophysical and biochemical properties and detected spectral response. We introduce improvements and new features to recently published hyperspectral leaf model HyperBlend. We present two methods for increasing simulation speed of the model up to 200 times faster with slight decrease in simulation accuracy. We integrate the well-known PROSPECT leaf model into HyperBlend allowing us to use the PROSPECT parametrization for leaf simulation. For the first time, we show that HyperBlend generalizes well and can be used to accurately simulate a wide variety of plant leaf spectra. HyperBlend is available as an open-source Python project under MIT license in a GitHub repository available at: https://github.com/silmae/hyperblend.

Anomaly detection from hyperspectral data needs computationally efficient methods to process the data when the data gathering platform is a drone or a cube satellite. In this study, we introduce a minimal learning machine for hyperspectral anomaly detection. Minimal learning machine is a novel distance-based classification algorithm, which is now modified to detect anomalies. Besides being computationally efficient, minimal learning machine is also easy to implement. Based on the results, we show that minimal learning machine is efficient in detecting global anomalies from the hyperspectral data with low false alarm rate.