![]() ![]() The acquisition and processing of hyperspectral images is also referred to as imaging spectroscopy or, with reference to the hyperspectral cube, as 3D spectroscopy.Īcquisition techniques for hyperspectral imaging, visualized as sections of the hyperspectral datacube with its two spatial dimensions (x,y) and one spectral dimension (lambda). If the pixels are too small, then the intensity captured by each sensor cell is low, and the decreased signal-to-noise ratio reduces the reliability of measured features. ![]() If the pixels are too large, then multiple objects are captured in the same pixel and become difficult to identify. However, spatial resolution is a factor in addition to spectral resolution. If the scanner detects a large number of fairly narrow frequency bands, it is possible to identify objects even if they are only captured in a handful of pixels. The precision of these sensors is typically measured in spectral resolution, which is the width of each band of the spectrum that is captured. However, for many development and validation studies, handheld sensors are used. Hyperspectral cubes are generated from airborne sensors like NASA's Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), or from satellites like NASA's EO-1 with its hyperspectral instrument Hyperion. Technically speaking, there are four ways for sensors to sample the hyperspectral cube: Spatial scanning, spectral scanning, snapshot imaging, and spatio-spectral scanning. These 'images' are combined to form a three-dimensional ( x, y, λ) hyperspectral data cube for processing and analysis, where x and y represent two spatial dimensions of the scene, and λ represents the spectral dimension (comprising a range of wavelengths). Each image represents a narrow wavelength range of the electromagnetic spectrum, also known as a spectral band. 3 Distinguishing hyperspectral from multispectral imagingįiguratively speaking, hyperspectral sensors collect information as a set of 'images'.For example, a spectral signature for oil helps geologists find new oil fields. Known as spectral signatures, these 'fingerprints' enable identification of the materials that make up a scanned object. Certain objects leave unique 'fingerprints' in the electromagnetic spectrum. Hyperspectral sensors look at objects using a vast portion of the electromagnetic spectrum. Įngineers build hyperspectral sensors and processing systems for applications in astronomy, agriculture, molecular biology, biomedical imaging, geosciences, physics, and surveillance. Hyperspectral imaging measures continuous spectral bands, as opposed to multiband imaging which measures spaced spectral bands. In hyperspectral imaging, the recorded spectra have fine wavelength resolution and cover a wide range of wavelengths. This technique of dividing images into bands can be extended beyond the visible. Whereas the human eye sees color of visible light in mostly three bands (long wavelengths - perceived as red, medium wavelengths - perceived as green, and short wavelengths - perceived as blue), spectral imaging divides the spectrum into many more bands. There are push broom scanners and the related whisk broom scanners (spatial scanning), which read images over time, band sequential scanners (spectral scanning), which acquire images of an area at different wavelengths, and snapshot hyperspectral imaging, which uses a staring array to generate an image in an instant. There are three general branches of spectral imagers. 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. Hyperspectral imaging collects and processes information from across the electromagnetic spectrum. ![]() Two-dimensional projection of a hyperspectral cube ![]()
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