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Coastal and Estuarine Waters: Optical Sensors and Remote Sensing
Published in Yeqiao Wang, Coastal and Marine Environments, 2020
Multispectral imaging is remote sensing that obtains optical representations in two or more ranges of frequencies or wavelengths. Multispectral imaging sensors capture image data from at least two or more wavelengths across the electromagnetic spectrum. On the sensor, each channel is sensitive to radiation within a narrow wavelength band resulting in a multilayer image (Figure 4.3) that contains both the brightness and spectral (color) information of the pixels sampled. In a multilayer image, data from each wavelength forms an image that carries some specific spectral information about the pixels in that image. By “stacking” images together from the same area, a multilayer image is formed composed of individual component images. Please note that multispectral images do not produce the spectrum of a pixel because wavelengths may be separated by filters or by the use of instruments that are sensitive to particular bands in the spectrum. Multispectral images are the main type of images acquired by most spaced-based or airborne radiometer systems. Radiometers are devices that measure the flux of electromagnetic radiation. Satellites may carry many radiometers in order to acquire data from selected portions of the electromagnetic spectrum. For example, one radiometer may acquire data from wavelengths in the red–green–blue (RGB) [700–400 nanometers (nm)] portion of the visible spectrum, a second radiometer may acquire data from wavelengths in the near infrared (700–3000 nm), and another might acquire data from mid-infrared to thermal region (greater than 3000 nm).
Hyperspectral image analysis for subcutaneous veins localization
Published in Ahmad Fadzil Mohamad Hani, Dileep Kumar, Optical Imaging for Biomedical and Clinical Applications, 2017
Aamir Shahzad, Mohamad Naufal Mohamad Saad, Fabrice Meriaudeau, Aamir Saeed Malik
Hyperspectral and multispectral imaging are well-established spectroscopy techniques in remote sensing, satellite imaging, agriculture, physics and military. In recent years, it has gained attention in the field of biomedical imaging, especially where the standard imaging techniques fail to provide the desired outcomes [23]. Hyperspectral sensor records spectroscopic information of the entire field of view for each band and combine the collected information as a data cube, as shown in Figure 7.7, containing the image of the scene for each wavelength or band [24]. Each pixel has a specific reflectance value for a certain wavelength. The three-dimensional data cube often called hypercube is formed with two spatial and one spectral dimension [25]. With higher spectral resolution, it provides the ability to analyse data on wavelength or sub-wavelength scale. Furthermore, it also provides the ability to see beyond the visible range giving more details of the scene. Hyperspectral venous imaging allows us to look deeper in the NIR window to determine the optimal wavelength range that ensures a high contrast between skin and veins. The goal is to acquire and process hyperspectral images in the visible and NIR ranges in order to define optimized illumination for all skin tones.
Technologies
Published in Henry H. Perritt, Eliot O. Sprague, Domesticating Drones, 2016
Henry H. Perritt, Eliot O. Sprague
Multispectral imaging is useful in agricultural surveys, because vegetation reflects not only the blue, green, and red colors perceivable by the human eye, but also near-infrared and mid-infrared light. Healthy vegetation is highly reflective in the near infrared spectrum. Overlaying the near infrared light intensity on a traditional core photograph highlights healthy vegetation, and contrasts it with less healthy vegetation that appears less green in the composite image. Soil reflects mid-infrared radiation according to its moisture content. So overlaying mid-infrared light intensity onto traditional core shots, highlight the moisture content of soil.
A fresh look at computer vision for industrial quality control
Published in Quality Engineering, 2022
Bart De Ketelaere, Niels Wouters, Ioannis Kalfas, Remi Van Belleghem, Wouter Saeys
The second development is related to the imaging system itself. As mentioned before, traditional imagers are based on silicium technology, inherently limiting their sensitivity to the 400−1000 nm range. Furthermore, this range is at most split into three different color regions (RGB) providing only a rough characterization of the scene that is imaged. This is especially true when realizing that a substantial number of organic molecules absorb light at specific wavelength that do not necessary align with the RGB bands. As such, the rough and rigid division of the light spectrum into three broad R, G and B bands may not provide sufficient information for all vision tasks. Instead, spectral cameras can split captured light into more, narrow color bands. Cameras capturing 4 to 10 distinct bands are typically termed ‘multispectral’, whereas ‘hyperspectral’ cameras capture a spectrum of up to several hundreds of bands, thus providing a detailed spectral fingerprint of the scene – for every pixel. Specialized cameras based on alternative detectors even allow to broaden the wavelength range in the (near) infrared beyond 1000 nm, providing more detailed chemical information of the sample, or allowing to visualize aspects that remain invisible to the human eye.
Demosaicing Method for Multispectral Images Using Derivative Operations
Published in American Journal of Mathematical and Management Sciences, 2021
Images with more number of spectral bands than RGB color images are called multi-spectral images. The multispectral images contain more information as compared to RGB color images. It has been found that multispectral images can be used in various fields that require more precise color representation, such as remote sensing (Addesso et al., 2017; Brenner et al., 2017; Galidaki et al., 2017; MacLachlan et al., 2017), satellite imaging (Kalkan et al., 2010; Mangai et al., 2010), medical imaging (Pearce et al., 2016; Shinoda et al., 2015), vegetation analysis, military surveillance, cyber forensics, etc. Acquiring raw multispectral images requires multi-camera-one-shot imaging system that is embedded with multiple cameras and various mechanical and optical parts. These systems are quite expensive and heavy, and therefore they are less preferred and their usage is limited (Monno et al., 2011; Yamaguchi et al., 2006).
Mapping crop types in fragmented arable landscapes using AVIRIS-NG imagery and limited field data
Published in International Journal of Image and Data Fusion, 2020
Eric Ariel L. Salas, Sakthi Kumaran Subburayalu, Brian Slater, Kaiguang Zhao, Bimal Bhattacharya, Rojalin Tripathy, Ayan Das, Rahul Nigam, Rucha Dave, Parshva Parekh
Remote sensing, and hyperspectral remote sensing in particular, offers great potential for identifying and mapping the diversity of land use and management regimes across the landscape (Daughtry et al. 2005). Over the last decade, research studies have relied on the rich spatial and spectral information of airborne hyperspectral imagery that rendered the discrimination of individual vegetation species feasible (Du 2007, Jones et al. 2010, Li et al. 2014) and delineate change of vegetation types (Möckel et al. 2014, Schuster et al. 2015). One advantage of hyperspectral imagery is its high spatial resolution that opens possibilities for mapping vegetation at local field-mapping scales. Another is its narrow bands that have the potential to be exploited for classification (e.g. shape of the spectral signature that is discriminative for particular plant species) (Salas and Subburayalu 2019). Note that the unique spectral characteristics of different vegetation types are crucial for identification and mapping of vegetation cover using remote sensing images. Deriving this unique spectral characteristic information from multispectral images is usually difficult, if not impossible, because of large bandwidths (Mather and Koch 2010). Hyperspectral images, however, capture hundreds of narrow spectral bands than multispectral images, which allow the differentiation of subtle differences among biophysical attributes of vegetation (Yang and Everitt 2010).