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Multispectral vs hyperspectral imaging for unmanned aerial vehicles: current and prospective state of affairs

Multispectral vs hyperspectral imaging for unmanned aerial vehicles: current and prospective state of affairs

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Over the past few decades of imaging, these sensing instruments are now more advanced with multiple missions such as surveillance, monitoring, tracking and destruction of spatial objects. Nowadays, unmanned aerial vehicles (UAVs) are much prevalent, which could acquire a comprehensive view and could perform actions even to the lowest target levels at the ground. The UAV can be developed with minimal cost than other remote mission. Hence, it is much cost-effective. This chapter aims at detailing the critical aspects of two different variants of remotesensing (RS) technologies in UAVs: (a) multispectral imaging (MSI) and (b) hyperspectral imaging, which accounts for the spatial and spectral signatures of the observed underlying natural phenomena.

Chapter Contents:

  • 7.1 Introduction
  • 7.2 UAV imaging architecture and components
  • 7.2.1 Future scope for UAV
  • 7.3 Multispectral vs. hyperspectral imaging instruments
  • 7.3.1 Multispectral imaging
  • 7.3.1.1 Low-resolution imaging
  • 7.3.1.2 High-resolution imaging
  • 7.3.2 Hyperspectral imaging
  • 7.3.3 Satellite imaging vs UAV imaging
  • 7.4 UAV image processing workflow
  • 7.4.1 Atmospheric correction
  • 7.4.2 Spectral influence mapping
  • 7.4.3 Dimensionality reduction
  • 7.4.4 Computational tasks
  • 7.5 Data processing toolkits for spatial data
  • 7.6 UAV open data sets for research–multispectral and hyperspectral
  • 7.7 Applications of MSI and HSI UAV imaging
  • 7.7.1 Agriculture monitoring
  • 7.7.2 Coastal monitoring
  • 7.7.3 Forestry
  • 7.7.4 Urban planning
  • 7.7.5 Defence applications
  • 7.7.6 Environmental monitoring
  • 7.7.7 Other commercial uses
  • 7.8 Conclusion and future scope
  • References

Inspec keywords: autonomous aerial vehicles; remotely operated vehicles; remote sensing

Other keywords: prospective state; spatial objects; multiple missions; UAV; comprehensive view; lowest target levels; multispectral vs hyperspectral imaging; remote mission; sensing instruments; current state; unmanned aerial vehicles

Subjects: Aerospace control; Optical, image and video signal processing; Mobile robots; Computer vision and image processing techniques

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