Design and development of a multi-purpose low-cost hyperspectral imaging system

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Abstract

Hyperspectral image analysis is gaining momentum in a wealth of natural resources and agricultural applications facilitated by the increased availability of low-cost imaging systems. In this study, we demonstrate the development of the Vegetation Mobile Mapping System (VMMS), a low-cost hyperspectral sensing system that is supported by consumer-grade digital camera(s). The system was developed using off-the-shelf imaging and navigation components mainly for ground-based applications. The system integrates a variety of components including timing and positioning GPS receivers and an Inertial Measurement Unit (IMU). The system was designed to be modular and interoperable allowing the imaging components to be used with different navigation systems. The technique used for synchronizing captured images with GPS time was presented. A relative radiometric calibration technique utilizing images of homogeneous targets to normalize pixel gain and offset parameters was used. An empirical spectral calibration method was used to assign wavelengths to image bands. Data acquisition parameters to achieve appropriate spatial coverage were presented. The system was tested in ground-based data collection and analysis experiments that included water quality and vegetation studies. © 2011 by the authors.

Figures

  • Figure 1. Data acquisition components for the hyperspectral sensor (top) and the digital cameras (bottom).
  • Figure 1. Cont.
  • Table 1. ImSpector Spectrograph and Imperx camera characteristics.
  • Figure 2. A schematic diagram showing hyperspectral image formation.
  • Figure 3. The results of applying relative detector calibration on band 10 (top) and band 18 (bottom) of one of the hyperspectral images captured by our sensor for a water body with floating and submerged reflection sheets.
  • Figure 3. Cont.
  • Figure 4. Empirical spectral calibration using Fluorescent light spectra (8 pixel spectral binning configuration). Fluorescent light spectra (left) and linear regression of wavelength against band number (right).
  • Table 2. Hyperspectral sensor spatial coverage at different vehicle speeds and frame acquisition rates.

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CITATION STYLE

APA

Abd-Elrahman, A., Pande-Chhetri, R., & Vallad, G. (2011). Design and development of a multi-purpose low-cost hyperspectral imaging system. Remote Sensing, 3(3), 570–586. https://doi.org/10.3390/rs3030570

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