New Hyperspectral Discrimination Measure for Spectral Characterization

By Yingzi Du, Chein-I. Chang, Hsuan Ren, Chein-Chi Chang, J. Jensen, F. D'Amico. 2004

363 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Atmospheric Science, Media Technology

View Paper: https://www.semanticscholar.org/paper/89440f2f7826f1d04ea7dfb6ce654ef5e2c9b816

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In Analytical Chemistry

In Atmospheric Science

In Media Technology

Citations in the corpus

Similarity measures for spectral discrimination of salt‐affected soils (2007, methodology): “…spectra are referred to as similarity measures (spectral matching), which are commonly applied to define (dis)similarity between spectra of an unknown substance (in this case salt-affected soil) and spectra of a reference substance (in this case non-saline soil) (van der Meer 2000, Du et al. 2004).”
A Review of Change Detection in Multitemporal Hyperspectral Images: Current Techniques, Applications, and Challenges (2019, methodology): “Other methods focus on the construction of binary representations based on similarity measures, such as the spectral angle mapper (SAM) [77] or spectral information divergence [78].”
‘Extended spectral angle mapping (ESAM)’ for citrus greening disease detection using airborne hyperspectral imaging (2014, methodology): “Qin et al. (2009) developed a SID based algorithms for HS image processing and classification to differentiate citrus canker lesions from normal and other diseased peel conditions.”
Hyperspectral image analysis techniques for the detection and classification of the early onset of plant disease and stress (2017, methodology): “This method compares the divergence between the observed spectra and a reference spectra (a library of spectra, or average spectra of interest from the data), where the smaller the divergence value then the more similar the spectra are, and if they are larger than a set threshold then they are not c”
Spectral matching accuracy in processing hyperspectral data (2005, methodology): “In addition, new measures such as Spectral Information Divergence [ 3 ] and Spectral Gradient [4] were proposed to better reflect the nature of hyperspectral data.”
Spectral difference analysis and airborne imaging classification for citrus greening infected trees (2012, background): “Since MinDist measures the Euclidean distance from library mean, and SAM is essentially the Euclidean distance (Du et al., 2004) when the spectral angle is small (which is exactly the case in this study because this HLB disease detection is actually based on the same kind of canopy), they produced v”