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Shadow detection and removal is a challenging problem in the analysis ofhyperspectral images. Yet, this step is crucial for analyzing data for remotesensing applications like methane detection. In this work, we develop a shadowdetection and removal method only based on the spectrum of each pixel and theoverall distribution of spectral values. We first introduce Iterative LogisticRegression (ILR) to learn a spectral basis in which shadows can be linearlyclassified. We then model the joint distribution of the mean radiance and theprojection coefficients of the spectra onto the above basis as a parametriclinear combination of Gaussians. We can then extract the maximum likelihoodmixing parameter of the Gaussians to estimate the shadow coverage and tocorrect the shadowed spectra. Our correction scheme reduces correctionartefacts at shadow borders. The shadow detection and removal method is appliedto hyperspectral images from MethaneAIR, a precursor to the satelliteMethaneSAT.

Hyperspectral shadow removal with Iterative Logistic Regression and latent Parametric Linear Combination of Gaussians