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Two-dimensional projection of a hyperspectral cube. Hyperspectral imaging collects and processes information from across the electromagnetic spectrum. [1] The goal of hyperspectral imaging is to obtain the spectrum for each pixel in the image of a scene, with the purpose of finding objects, identifying materials, or detecting processes.
In hyperspectral imaging, a complete spectrum or some spectral information (such as the Doppler shift or Zeeman splitting of a spectral line) is collected at every pixel in an image plane. A hyperspectral camera uses special hardware to capture hundreds of wavelength bands for each pixel, which can be interpreted as a complete spectrum.
The passive hyperspectral imaging spectroscopy remote sensor observes a target in multi-spectral bands. The HSI camera separates the image spectra into 52 "bins" from 500 nanometers (nm) wavelength at the blue end of the visible spectrum to 1100 nm in the infrared, giving the camera a spectral resolution of 11.5 nm. [22]
In imaging spectroscopy (also hyperspectral imaging or spectral imaging) each pixel of an image acquires many bands of light intensity data from the spectrum, instead of just the three bands of the RGB color model. More precisely, it is the simultaneous acquisition of spatially coregistered images in many spectrally contiguous bands.
The technique was designed to put into practice the concept of 'tilted sampling' of the hyperspectral data cube, which had been deemed difficult to achieve. [4] Spatio-spectral scanning yields a series of thin, diagonal slices of the data cube. Figuratively speaking, each acquired image is a 'rainbow-colored' spatial map of the scene. More ...
It is also an important factor in multispectral imaging and hyperspectral imaging used in remote sensing [12] because water vapor absorbs radiation differently in different spectral bands. Its effects are also an important consideration in infrared astronomy and radio astronomy in the microwave or millimeter wave bands.
Integral field spectrography (IFS) techniques were the first snapshot hyperspectral imaging techniques to be developed. Since then, other snapshot hyperspectral imaging techniques, based for example on tomographic reconstruction [1] or compressed sensing using a coded aperture, [2] have been developed. [3]
Subcategories of multispectral remote sensing include hyperspectral, in which hundreds of bands are collected and analyzed, and ultraspectral remote sensing where many hundreds of bands are used (Logicon, 1997). The main purpose of multispectral imaging is the potential to classify the image using multispectral classification.
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