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

TL;DR

This paper presents a novel hyperspectral shadow removal method using Iterative Logistic Regression and a Gaussian mixture model, improving shadow detection and correction for remote sensing applications.

Contribution

It introduces a new spectral basis learning method and a parametric Gaussian mixture model for effective shadow detection and removal in hyperspectral images.

Findings

Reduces correction artifacts at shadow borders.

Successfully applied to MethaneAIR hyperspectral data.

Enhances accuracy of spectral analysis in shadowed regions.

Abstract

Shadow detection and removal is a challenging problem in the analysis of hyperspectral images. Yet, this step is crucial for analyzing data for remote sensing applications like methane detection. In this work, we develop a shadow detection and removal method only based on the spectrum of each pixel and the overall distribution of spectral values. We first introduce Iterative Logistic Regression (ILR) to learn a spectral basis in which shadows can be linearly classified. We then model the joint distribution of the mean radiance and the projection coefficients of the spectra onto the above basis as a parametric linear combination of Gaussians. We can then extract the maximum likelihood mixing parameter of the Gaussians to estimate the shadow coverage and to correct the shadowed spectra. Our correction scheme reduces correction artefacts at shadow borders. The shadow detection and removal method is applied to hyperspectral images from MethaneAIR, a precursor to the satellite MethaneSAT.