» Articles » PMID: 37896715

A Customisable Data Acquisition System for Open-Source Hyperspectral Imaging

Overview
Journal Sensors (Basel)
Publisher MDPI
Specialty Biotechnology
Date 2023 Oct 28
PMID 37896715
Authors
Affiliations
Soon will be listed here.
Abstract

Hyperspectral imagers, or imaging spectrometers, are used in many remote sensing environmental studies in fields such as agriculture, forestry, geology, and hydrology. In recent years, compact hyperspectral imagers were developed using commercial-off-the-shelf components, but there are not yet any off-the-shelf data acquisition systems on the market to deploy them. The lack of a self-contained data acquisition system with navigation sensors is a challenge that needs to be overcome to successfully deploy these sensors on remote platforms such as drones and aircraft. Our work is the first successful attempt to deploy an entirely open-source system that is able to collect hyperspectral and navigation data concurrently for direct georeferencing. In this paper, we describe a low-cost, lightweight, and deployable data acquisition device for the open-source hyperspectral imager (OpenHSI). We utilised commercial-off-the-shelf hardware and open-source software to create a compact data acquisition device that can be easily transported and deployed. The device includes a microcontroller and a custom-designed PCB board to interface with ancillary sensors and a Raspberry Pi 4B/NVIDIA Jetson. We demonstrated our data acquisition system on a Matrice M600 drone at a beach in Sydney, Australia, collecting timestamped hyperspectral, navigation, and orientation data in parallel. Using the navigation and orientation data, the hyperspectral data were georeferenced. While the entire system including the pushbroom hyperspectral imager and housing weighed 735 g, it was designed to be easy to assemble and modify. This low-cost, customisable, deployable data acquisition system provides a cost-effective solution for the remote sensing of hyperspectral data for everyone.

References
1.
Lee Z, Carder K, Mobley C, Steward R, Patch J . Hyperspectral remote sensing for shallow waters. I. A semianalytical model. Appl Opt. 2008; 37(27):6329-38. DOI: 10.1364/ao.37.006329. View

2.
Stuart M, McGonigle A, Willmott J . Hyperspectral Imaging in Environmental Monitoring: A Review of Recent Developments and Technological Advances in Compact Field Deployable Systems. Sensors (Basel). 2019; 19(14). PMC: 6678368. DOI: 10.3390/s19143071. View

3.
Chen J, Cai F, He R, He S . Experimental Demonstration of Remote and Compact Imaging Spectrometer Based on Mobile Devices. Sensors (Basel). 2018; 18(7). PMC: 6068658. DOI: 10.3390/s18071989. View

4.
Lee Z, Carder K, Mobley C, Steward R, Patch J . Hyperspectral remote sensing for shallow waters. 2. Deriving bottom depths and water properties by optimization. Appl Opt. 2008; 38(18):3831-43. DOI: 10.1364/ao.38.003831. View

5.
Morales A, Horstrand P, Guerra R, Leon R, Ortega S, Diaz M . Laboratory Hyperspectral Image Acquisition System Setup and Validation. Sensors (Basel). 2022; 22(6). PMC: 8956094. DOI: 10.3390/s22062159. View