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Military optoelectronics
Published in P. Dakin John, G. W. Brown Robert, Handbook of Optoelectronics, 2017
The wavelength range from 400 to about 1700 nm is referred to as the “visible and near-infrared” (VNIR) region. VNIR image sensors depend on reflected light, using TV and image intensifier technology. Early active VNIR imaging systems used IR illuminators to augment natural light. Detection and recognition of objects of interest depends on the contrast between the object of interest and its surroundings, and on the resolution of the sensor. Modern high-resolution TV technology now allows VNIR imaging systems to provide resolution approaching, but not quite matching, that obtained by the human eye through a high-power magnifying sight of similar aperture and field of view (FOV).
Introduction to Remote Sensing
Published in Caiyun Zhang, Multi-sensor System Applications in the Everglades Ecosystem, 2020
Both point and imaging HRS data can be acquired using hyperspectral sensors. Currently the ASD spectroradiometer is mainly used to collect field and laboratory HRS data. It is specifically designed for environment remote sensing to acquire Visible and Near Infrared (VNIR) and Shortwave Infrared (SWIR) spectra. The ASD spectroradiometer collects data in the range of 350 nm to 2500 nm with different spectral resolution by using different models such as FieldSpec hi-Res and wide-res spectroradiometers. It can measure spectral reflectance, transmittance, absorbance, radiance, and irradiance of the samples. The instrument communicates with a computer that can record the spectral measures using a specifically developed software package RS3. The measured spectra can be viewed and processed using the related ViewSpec ProTM package. In addition, the ASD derived spectra binary file can be imported directly into other remote sensing software packages such as ENVI to build a spectral library that can be used as a reference for material identification in remote sensing images. The USGS has built spectral libraries for minerals, rocks, soils, physically constructed as well as mathematically computed mixtures, plants, vegetation communities, microorganisms, and manmade materials (http://speclab.cr.usgs.gov/spectral-lib.html). Other libraries include the Jet Propulsion Laboratory’s (JPL’s) mineral library, Johns Hopkins University’s Spectral Library, IGCP 264’s Spectral Library, and University of Texas at El Paso’s Vegetation Spectral Library. Our research lab built a coastal sand spectral library using the ASD spectroradiometer. The collected field spectra can be used to calibrate remote sensing imagery and characterize target materials. Figure 2.14 shows an ASD spectroradiometer and C. Zhang using the instrument to conduct field measures.
Reflectance spectroscopy and ASTER mapping of aeolian dunes of Shaqra and Tharmada Provinces, Saudi Arabia: Field validation and laboratory confirmation
Published in International Journal of Image and Data Fusion, 2023
Yousef Salem, Habes Ghrefat, Rajendran Sankaran
In addition, the spectral reflectances of the different sand fractions are measured in the visible to shortwave infrared (VNIR-SWIR, 0.4–2.5 μm) regions of the electromagnetic spectrum using a GER3700 spectrometer in the laboratory to understand the spectral absorption of minerals of the dunes and map the dunes. The measurement is carried out keeping the instrument vertically above the samples. The samples are illuminated at an incident angle of 30° and reflectances are measured in a rectangular field of view of 1.5 by 7 cm. The GER3700 spectrometer measures 640 bands between 0.315 and 2.519 μm at the spectral sampling range from 0.0015 to 0.012 μm (Ghrefat et al. 2007). The spectral radiance (W/m2/sr/nm) of a Spectralon (calibration material) is used as a reference to measure the spectral radiance of samples. The reflectance of samples is calculated from the ratio of two spectral radiance that is by dividing the radiance of the Spectralon by the radiance of the measured target.
Dynamic status of land surface temperature and spectral indices in Imphal city, India from 1991 to 2021
Published in Geomatics, Natural Hazards and Risk, 2021
Arun Mondal, Subhanil Guha, Sananda Kundu
Satellite remote sensing techniques are applied to detect the changed land surface zones by using their visible to near-infrared (VNIR) and shortwave infrared (SWIR) bands (Chen et al. 2006). Moreover, thermal infrared (TIR) bands are also used to create some spectral indices (Kalnay and Cai 2003; Du et al. 2016; Berger et al. 2017; He et al. 2019). The most popular index for vegetation in LST estimation is the normalized difference vegetation index (NDVI) (Carlson and Ripley 1997; Sobrino et al. 2004). In mixed urban land, high LST is related to low vegetal covered area (Voogt and Oke 2003). Many studies are based on the LST-NDVI correlation (Gutman and Ignatov 1998; Guha and Govil 2021) are available to explore the pattern of LST. Modified normalized difference water index (MNDWI) is one of the most used water indices and it is considerably used in LST-related research works (Essa et al. 2012; Guha et al. 2017). Normalized difference built-up index (NDBI) is the most popular built-up index that is invariably used in LST-related studies (Zha et al. 2003; Guha et al. 2020). Normalized difference bareness index (NDBaI) is an index for bare land identification (Zhao and Chen 2005; Chen et al. 2006; Guha and Govil 2021).
Partial sub-pixel and pixel-based alteration mapping of porphyry system using ASTER data: regional case study in western Yazd, Iran
Published in International Journal of Image and Data Fusion, 2019
Amir Taghavi, Mohammad Maanijou, David Lentz, Ali-Asghar Sepahi
PCA reduces data dimensions (Haykin 1999) by a mathematical formulation (Pearson 1901, Jolliffe 1986). The characteristics of reduced dimension computational structure are identified with little loss of information (Ye et al. 2004). Advantages of that reduction are: for data representation, image compression, calculation reduction necessary in subsequent processing, etc. According to Smith (2002), PCA is an authentic algorithm for image compression with minimal loss of data. Singh and Harrison (1985) noted that PCA is a multivariate statistical procedure that selects sets of linearly uncorrelated (eigenvector loadings) variables in such a way that each component possibly correlated variables and has a smaller variance. The idea of applying PCA to mapping altered minerals abundance using the electromagnetic spectrum in SWIR, VNIR, and thermal infrared (TIR) ranges was proposed by Crósta et al. (1996) and Prado and Crósta (1997) to mapping goethite, hematite, calcite–chlorite, muscovite–kaolinite, and silica alteration minerals. Crósta and De Souza Filho (2003) used PCA on subsets of four SWIR and VNIR portions of ASTER bands in order to target key alteration minerals (i.e. alunite, illite, kaolinite-smectite, and kaolinite) related to the epithermal gold deposits in Argentina.