Red Teaming Models for Hyperspectral Image Analysis Using Explainable AI

arXiv:2403.08017v1 Announce Type: new
Abstract: 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 …

abstract advance analysis applications arxiv biases cs.ai cs.cv demand domain explainable ai fields flaws gap image machine machine learning making quality red teaming robust sensing space type vital