hyperspectralimaging

1. By-
A.S
AITEJA
11261A0
501

2.  Introduction
 What is Hyperspectral Imaging?
 How does Hyperspectral Imaging work?
 Acquisition Techniques for Hyperspectral Imaging
 Advantages
 Disadvantages
 Applications
 Softwares Used
 Conclusion

3. INTRODUCTION

4.  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.

5. Fig: Two-dimensional projection of a hyperspectral cube

6. What is Hyperspectral
Imaging?

7.  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.

8. Successive scan lines
Three-dimensional
Hyperspectral cube is
assembled by stacking
two-dimensional
spatial-spectral
scan lines
Spatial
pixels
Spectral channels
x
y
z

11. How does
Hyperspectral Imaging
Work?

12.  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.

13.  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.

16. Acquisition Techniques
for Hyperspectral Imaging

17.  Spatial Scanning
 Spectral Scanning
 Non-Scanning
 Spatiospectral Scanning

18. 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).

19. Advantages

20.  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.

21. Disadvantages

22.  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

23. Applications

24.  Agriculture
 Astronomy
 Chemical Imaging
 Eye care
 Food Processing
 Mineralogy
 Remote Sensing
 Surveillance

25. Softwares Used

26. 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.


27. Conclusion

28.  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”.