Red Teaming Models for Hyperspectral Image Analysis Using Explainable AI
This addresses the need for robust and quality-assured models in remote sensing applications, particularly for soil parameter estimation, though it is incremental in integrating red teaming strategies into this domain.
The paper tackled the problem of ensuring reliability in machine learning models for hyperspectral image analysis by red teaming a top-performing model from the HYPERVIEW challenge, using Explainable AI to identify flaws and achieve comparable performance with only 1% of input features and up to a 5% performance loss.
Remote sensing (RS) applications in the space domain demand machine learning (ML) models that are reliable, robust, and quality-assured, making red teaming a vital approach for identifying and exposing potential flaws and biases. Since both fields advance independently, there is a notable gap in integrating red teaming strategies into RS. This paper introduces a methodology for examining ML models operating on hyperspectral images within the HYPERVIEW challenge, focusing on soil parameters' estimation. We use post-hoc explanation methods from the Explainable AI (XAI) domain to critically assess the best performing model that won the HYPERVIEW challenge and served as an inspiration for the model deployed on board the INTUITION-1 hyperspectral mission. Our approach effectively red teams the model by pinpointing and validating key shortcomings, constructing a model that achieves comparable performance using just 1% of the input features and a mere up to 5% performance loss. Additionally, we propose a novel way of visualizing explanations that integrate domain-specific information about hyperspectral bands (wavelengths) and data transformations to better suit interpreting models for hyperspectral image analysis.