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Multispectral and hyperspectral differences. Hyperspectral imaging is part of a class of techniques commonly referred to as spectral imaging or spectral analysis. The term “hyperspectral imaging” derives from the development of NASA's Airborne Imaging Spectrometer (AIS) and AVIRIS in the mid-1980s.
Multispectral imaging has also found use in document and painting analysis. [3] [4] Multispectral imaging measures light in a small number (typically 3 to 15) of spectral bands. Hyperspectral imaging is a special case of spectral imaging where often hundreds of contiguous spectral bands are available. [5]
A figure illustrating the differences between multi-and hyperspectral imaging. 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.
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.
Hyperspectral images are often represented as an image cube, which is type of data cube. [3] Applications of spectral imaging [4] include art conservation, astronomy, solar physics, planetology, and Earth remote sensing. It also applies to digital and print reproduction, and exhibition lighting design for small and medium cultural institutions. [5]
Remote sensing can be divided into two types of methods: Passive remote sensing and Active remote sensing. Passive sensors gather radiation that is emitted or reflected by the object or surrounding areas. Reflected sunlight is the most common source of radiation measured by passive sensors.
Due to advances in hyperspectral remote sensing technology, high-resolution reflectance spectrums are now available, which can be used with traditional multispectral VIs. In addition, VIs have been developed to be used specifically with hyperspectral data, such as the use of Narrow Band Vegetation Indices.
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 ...
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