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Hyperspectral Imaging

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Multispectral remote sensors such as the Landsat Thematic Mapper and SPOT XS produce

images with a few relatively broad wavelength bands. Hyperspectral remote sensors, on the

other hand, collect image data simultaneously in dozens or hundreds of narrow, adjacent

spectral bands. These measurements make it possible to derive a continuous spectrum for each

image cell, as shown in the illustration below. After adjustments for sensor, atmospheric, and

terrain effects are applied, these image spectra can be compared with field or laboratory

reflectance spectra in order to recognize and map surface materials such as particular types of

vegetation or diagnostic minerals associated with ore deposits.

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Hyperspectral Imaging

  1. 1. By- PARIKSHITH BEENAVENI 13951A0476
  2. 2.  Introduction  What is Hyperspectral Imaging?  How does Hyperspectral Imaging work?  Acquisition Techniques for Hyperspectral Imaging  Advantages  Disadvantages  Applications  Softwares Used  Conclusion
  3. 3.  Imaging is the visual representation of an object’s form.  Spectral imaging is a branch of spectroscopy in which a complete spectrum or some spectral information is collected at every location on image plane and is processed.  The term Hyperspectral imaging comes under Spectral imaging.  Hyperspectral images are produced by instruments called Imaging spectrometers.  Spectral images are often represented as an image cube, a type of data cube.
  4. 4. Fig: Two-dimensional projection of a hyperspectral cube
  5. 5.  Hyperspectral imaging belongs to a class of techniques commonly referred to as spectral imaging or spectral analysis.  Hyperspectral imaging is the collecting and processing of information from across the electromagnetic spectrum.  Human eye sees visible light in three bands, i.e. red, green, and blue whereas spectral imaging divides the spectrum into many more bands.
  6. 6. Successive scan lines Three-dimensional Hyperspectral cube is assembled by stacking two-dimensional spatial-spectral scan lines Spatial pixels Spectral channels x y z
  7. 7.  Hyperspectral imaging deals with the imaging of narrow spectral bands over a continuous spectral range, and produces the spectra of all pixels in the scene.  Hyperspectral sensors collect information as a set of ‘images’.  These 'images' are then combined and formed into a three-dimensional hyperspectral data cube for processing and analysis.
  8. 8.  Hyperspectral imaging does not just measure each pixel in the image, but also measures the reflection, emission and absorption of electromagnetic radiation.  It provides a unique spectral signature for every pixel, which can be used by processing techniques to identify and discriminate materials.
  9. 9.  Spatial Scanning  Spectral Scanning  Non-Scanning  Spatiospectral Scanning
  10. 10. Fig: Acquisition techniques for hyperspectral imaging, visualized as sections of the hyperspectral datacube with its two spatial dimensions (x,y) and one spectral dimension (lambda).
  11. 11.  The primary advantage to hyperspectral imaging is that, because an entire spectrum is acquired at each point, the operator needs no prior knowledge of the sample, and postprocessing allows all available information from the dataset to be mined.  Hyperspectral imaging can also take advantage of the spatial relationships among the different spectra in a neighbourhood, allowing more elaborate spectral-spatial models for a more accurate segmentation and classification of the image.
  12. 12.  The primary disadvantages are cost and complexity.  Fast computers, sensitive detectors, and large data storage capacities are needed for analyzing hyperspectral data.  Also, one of the hurdles researchers have had to face is finding ways to program hyperspectral satellites to sort through data on their own and transmit only the most important images, as both transmission and storage of that many data could prove difficult and costly
  13. 13.  Agriculture  Astronomy  Chemical Imaging  Eye care  Food Processing  Mineralogy  Remote Sensing  Surveillance
  14. 14. Open source:  HyperSpy(software)Python Hyperspectral Toolbox.  Gerbil (software) hyperspectral visualization and analysis framework. Commercial:  Erdas Imagine, a remote sensing application for geospatial applications.  ENVI a remote sensing application.  MIA Toolbox multivariate image analysis.  MicroMSI a remote sensing application.  A Matlab Hyperspectral Toolbox.  Other Hyperspectral tools in MATLAB.  MountainsMap HyperSpectral, a version of MountainsMap dedicated to the analysis of hyperspectral data in microscopy.  Opticks a remote sensing application.  Scyllarus, hyperspectral imaging C++ API, MATLAB Toolbox and visualize. 
  15. 15.  Active area of Research and Development.  With hundreds of spectral channels now available, the sampled pixel spectra contain enough detail to allow spectroscopic principles to be applied for image understanding.  Requires an understanding of the nature and limitations of the data and of various strategies for processing and interpreting it.  “If a picture is worth 1000 words, a hyperspectral image is worth almost 1000 pictures”.
  • shefali8shekhawat95

    Aug. 22, 2021

Multispectral remote sensors such as the Landsat Thematic Mapper and SPOT XS produce images with a few relatively broad wavelength bands. Hyperspectral remote sensors, on the other hand, collect image data simultaneously in dozens or hundreds of narrow, adjacent spectral bands. These measurements make it possible to derive a continuous spectrum for each image cell, as shown in the illustration below. After adjustments for sensor, atmospheric, and terrain effects are applied, these image spectra can be compared with field or laboratory reflectance spectra in order to recognize and map surface materials such as particular types of vegetation or diagnostic minerals associated with ore deposits.

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