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RSS FeedsRemote Sensing, Vol. 14, Pages 4914: Hyperspectral Reconnaissance: Joint Characterization of the Spectral Mixture Residual Delineates Geologic Unit Boundaries in the White Mountains, CA (Remote Sensing)

 
 

1 october 2022 11:06:23

 
Remote Sensing, Vol. 14, Pages 4914: Hyperspectral Reconnaissance: Joint Characterization of the Spectral Mixture Residual Delineates Geologic Unit Boundaries in the White Mountains, CA (Remote Sensing)
 


We use a classic locale for geology education in the White Mountains, CA, to demonstrate a novel approach for using imaging spectroscopy (hyperspectral imaging) to generate base maps for the purpose of geologic mapping. The base maps produced in this fashion are complementary to, but distinct from, maps of mineral abundance. The approach synthesizes two concepts in imaging spectroscopy data analysis: the spectral mixture residual and joint characterization. First, the mixture residual uses a linear, generalizable, and physically based continuum removal model to mitigate the confounding effects of terrain and vegetation. Then, joint characterization distinguishes spectrally distinct geologic units by isolating residual, absorption-driven spectral features as nonlinear manifolds. Compared to most traditional classifiers, important strengths of this approach include physical basis, transparency, and near-uniqueness of result. Field validation confirms that this approach can identify regions of interest that contribute significant complementary information to PCA alone when attempting to accurately map spatial boundaries between lithologic units. For a geologist, this new type of base map can complement existing algorithms in exploiting the coming availability of global hyperspectral data for pre-field reconnaissance and geologic unit delineation.


 
70 viewsCategory: Geology, Physics
 
Remote Sensing, Vol. 14, Pages 4913: On the Sensitivity of a Ground-Based Tropospheric Lidar to Aitken Mode Particles in the Upper Troposphere (Remote Sensing)
Remote Sensing, Vol. 14, Pages 4917: An Improved RANSAC Outlier Rejection Method for UAV-Derived Point Cloud (Remote Sensing)
 
 
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