Tag Archives: atmospheric correction
Atmospheric correction of vegetation reflectance with simulation-trained deep learning for ground-based hyperspectral remote sensing
We propose a framework for obtaining the vegetation spectral reflectance from at-sensor spectral radiance, which relies on a time-dependent Encoder-Decoder Convolutional Neural Network trained and tested using simulated spectra generated from radiative transfer modeling.
The Effects of Atmospheric Modeling Covariance on Ground-Based Hyperspectral Measurements of Surface Reflectance
This paper presents a novel framework for estimating the covariance and uncertainties of atmospheric parameters and the reflectance spectra of urban surfaces in high-resolution ground-based hyperspectral images.