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.

1. By-
PARIKSHITH BEENAVENI
13951A0476

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

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. Fig: Two-dimensional projection of a hyperspectral cube

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

9.  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.

10.  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.

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

14. 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).

15.  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.

16.  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

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

18. 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.


19.  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”.