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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.
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]
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]
Normalised Difference Vegetation Index (NDVI): The most commonly used remote sensing index [19] that calculates the ratio of the difference and sum between the Near Infrared and Red bands of multispectral images. It normally takes values between -1 and +1. It is mostly used in vegetation dynamics monitoring, [20] including biomass quantification.
The MODIS instruments were built by Santa Barbara Remote Sensing. [1] They capture data in 36 spectral bands ranging in wavelength from 0.4 μm to 14.4 μm and at varying spatial resolutions (2 bands at 250 m, 5 bands at 500 m and 29 bands at 1 km). Together the instruments image the entire Earth every 1 to 2 days.
Most remote sensing applications process digital images to extract spectral signatures at each pixel and use them to divide the image in groups of similar pixels (segmentation) using different approaches. As a last step, they assign a class to each group (classification) by comparing with known spectral signatures.
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