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Multispectral imaging captures image data within specific wavelength ranges across the electromagnetic spectrum. The wavelengths may be separated by filters or detected with the use of instruments that are sensitive to particular wavelengths, including light from frequencies beyond the visible light range (i.e. infrared and ultraviolet ).
Multispectral images do not produce the "spectrum" of an object. Landsat is a prominent practical example of multispectral imaging. Hyperspectral deals with imaging narrow spectral bands over a continuous spectral range, producing the spectra of all pixels in the scene.
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.
In other words, the camera has a high spectral resolution. The phrase "spectral imaging" is sometimes used as a shorthand way of referring to this technique, but it is preferable to use the term "hyperspectral imaging" in places when ambiguity may arise. Hyperspectral images are often represented as an image cube, which is type of data cube. [3]
A hyperspectral sensor collects spectral data in a continuous spectrum whereas a multispectral sensor collects spectral data in varying bandwidths in the EM spectrum. In modern times, multi- and hyperspectral imaging sensors are mainly adopted in spectroradiometry.
Hyperspectral data is often used to determine what materials are present in a scene. Materials of interest could include roadways, vegetation, and specific targets (i.e. pollutants, hazardous materials, etc.). Trivially, each pixel of a hyperspectral image could be compared to a material database to determine the type of material making up the ...
Typically, dementia is associated with classic symptoms like confusion and memory loss. But new research finds that there could be a less obvious risk factor out there: your cholesterol levels ...
Commercially available laboratory-based chemical imaging systems emerged in the early 1990s (ref. 1-5). In addition to economic factors, such as the need for sophisticated electronics and extremely high-end computers, a significant barrier to commercialization of infrared imaging was that the focal plane array (FPA) needed to read IR images were not readily available as commercial items.
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