HYPERSPECTRAL IMAGING
#1

Presented by
Pradeep R

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ABSTRACT
Hyperspectral imaging has been an area of active research and development, and hyperspectral images have been available only to researchers. With the recent appearance of commercial airborne hyperspectral imaging systems, hyperspectral imaging is poised to enter the mainstream of remote sensing. Hyperspectral images will find many applications in resource management, agriculture, mineral exploration, and environmental monitoring. But effective use of hyperspectral images requires an understanding of the nature and limitations of the data and of various strategies for processing and interpreting it. This booklet aims to provide an introduction to the fundamental concepts in the field of hyperspectral imaging.
Since human vision is limited to electromagnetic radiation in the frequency band of 400 790 terahertz visible light to the human eye is restricted to wavelengths in the range of 400 700 nm On the other hand, with 12 color channels, the eyes of mantis shrimp have the capability of viewing electromagnetic radiation ranging from ultraviolet (UV: 10 nm 400 nm) to infrared (Near IR: 700 nm 1,000 nm). In other words, mantis shrimp has the hyperspectral vision. With the recent rapid advances of satellite, senor, and computing technologies, it is now possible to capture and analyze hyperspectral image (HSI) data. We will discuss the significance of hyperspectral imaging for a broad spectrum of applications: from agricultural monitoring to geospatial mapping, and from security surveillance to cancerous tissue detection in medical imaging. However, the mathematics of HSI is most challenging and requires introduction of innovative ideas, since the amount of data information in a typical HSI cube is huge.
INTRODUCTION
Hyperspectral imaging collects and processes information from across electromagnetic spectrum. The most significant recent breakthrough in remote sensing has been the development of hyperspectral sensors and software to analyze the resulting image data. Fifteen years ago only spectral remote sensing experts had access to hyperspectral images or software tools to take advantage of such images. Over the past decade hyperspectral image analysis has matured into one of the most powerful and fastest growing technologies in the field of remote sensing.
The “hyper” in hyperspectral means ‘over’ as in ‘too many’ and refers to the large number of measured wavelength bands. Hyperspectral images are spectrally over determined, which means that they provide ample spectral information to identify and distinguish spectrally unique materials.
Hyperspectral imagery provides the potential for more accurate and detailed information extraction than possible with any other type of remotely sensed data.
While multispectral images have been in regular use the widespread use of hyperspectral images is a relatively recent trend. Hyperspectral imaging, also known as imaging spectrometry, is now a reasonably familiar concept in the world of remote sensing. However, for many remote sensing specialists who have not yet had the opportunity to use hyperspectral imagery in their work, the benefits of hyperspectral imagery may still be vague. Through this article, I hope your interest in this promising technology will be sparked as you learn about the fascinating detail available in hyperspectral imagery; detailed information that is being harvested by an increasing number of investigators. Their stories will likely persuade you that hyperspectral imagery is another power tool that belongs in your own remote sensing toolbox.
WORKING
Hyperspectral sensors collect information as a set of 'images'. Each image represents a range of the electromagnetic spectrum and is also known as a spectral band.
These 'images' are then combined and form a three dimensional hyperspectral cube for processing and analysis. Hyperspectral cubes are generated from airborne sensors like the NASA’s Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), or from satellites like NASA’s Hyperion.
The precision of these sensors is typically measured in spectral resolution, which is the width of each band of the spectrum that is captured.
If the scanner picks up on a large number of fairly narrow frequency bands, it is possible to identify objects even if said objects are only captured in a handful of pixels. However, spatial resolution is a factor in addition to spectral resolution.
If the pixels are too large, then multiple objects are captured in the same pixel and become difficult to identify. If the pixels are too small, then the energy captured by each sensor-cell is low, and the decreased signal-to-noise ratio reduces the reliability of measured features.
Hyperspectral images are spectrally over determined they provide ample spectral information to identify and distinguish between spectrally similar (but unique) materials. Consequently, hyperspectral imagery provides the potential for more accurate and detailed information extraction than is possible with other types of remotely sensed data.
Most multispectral imagers (e.g. Landsat, SPOT, AVHRR) measure reflectance of Earth’s surface material at a few wide wavelength bands separated by spectral segments where no measurements are taken. In contrast, most hyperspectral sensors measure reflected radiation as a series of narrow and contiguous wavelength bands.
Hyperspectral imaging measure reflected radiation at a series of narrow and contiguous wavelength bands resulting in a complete spectrum for each pixel.
When the spectrum for a single pixel in hyperspectral imagery is displayed it appears much like a spectrum measured in a spectroscopy laboratory. This type of detailed pixel spectrum can provide much more information about a surface than is available in a traditional multispectral pixel spectrum. When we look at a spectrum for one pixel in a hyperspectral image, it looks very much like a spectrum that would be measured in a spectroscopy laboratory.
Although most hyperspectral sensors measure hundreds of wavelengths, it is not the number of measured wavelengths that defines a sensor as hyperspectral. Rather it is the narrowness and contiguous nature of the measurements. For example, a sensor that measured only 20 bands could be considered hyperspectral if those bands were contiguous and, say, 10 nm wide. If a sensor measured 20 wavelength bands that were, say, 100 nm wide, or that were separated by non-measured wavelength ranges, the sensor would no longer be considered hyperspectral.
Standard multispectral image classification techniques were generally developed to classify multispectral images into broad categories. Hyperspectral imagery provides an opportunity for more detailed image analysis. For example, using hyperspectral data, spectrally similar materials can be distinguished, and sub-pixel scale information can be extracted. To fulfill this potential, new image processing techniques have been developed. Hyperspectral imaging is part of a class of techniques commonly referred to as spectral imaging or spectral analysis. Hyperspectral imaging is related to multispectral imaging.
Pixel spectra from an AVIRIS hyperspectral image. The red spectrum is from a pixel filled with the mineral Auntie, and the white spectrum is from a pixel filled with the mineral Kaolinite.
The distinction between hyper- and multi-spectral should not be based on a random or arbitrary "number of bands". A distinction that is based on the type of measurement may be more appropriate.
Multispectral deals with several images at discrete and somewhat narrow bands. The "discrete and somewhat narrow" is what distinguishes multispectral in the visible from color photography. A multispectral sensor may have many bands covering the spectrum from the visible to the long wave infrared. Multispectral images do not produce the "spectrum" of an object. Landsat is an excellent example.
Hyperspectral deals with imaging narrow spectral bands over a contiguous spectral range, and produce the spectra of all pixels in the scene. So a sensor with only 20 bands can also be hyperspectral when it covers the range from 500 to 700 nm with 20 10-nm wide bands. (While a sensor with 20 discrete bands covering the VIS, NIR, SWIR, MWIR, and LWIR would be considered multispectral.)
Ultraspectral could be reserved for interferometer type imaging sensors with a very fine spectral resolution. These sensor often have (but not necessarily) a low spatial resolution of several pixels only, a restriction imposed by the high data rate.
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#2
plz provide a ppt also dude
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to get information about the topic"HYPERSPECTRAL IMAGING" refer the page link bellow

http://studentbank.in/report-hyperspectr...1#pid57591
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thanks moderator i hav checked it but no ppt only documentation is available
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#5

no ppt available plz try to upload ppt moderator
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