Leveraging Activation Maximization and Generative Adversarial Training to Recognize and Explain Patterns in Natural Areas in Satellite Imagery
Ahmed Emam, Timo T. Stomberg, Ribana Roscher ·
Natural protected areas are vital for biodiversity, climate change mitigation, and supporting ecological processes. Despite their significance, comprehensive mapping is hindered by a lack of understanding of their characteristics and a missing land cover class definition. This paper aims to advance the explanation of the designating patterns forming protected and wild areas. To this end, we propose a novel framework that uses activation maximization and a generative adversarial model. With this, we aim to generate satellite images that, in combination with domain knowledge, are capable of offering complete and valid explanations for the spatial and spectral patterns that define the natural authenticity of these regions. Our proposed framework produces more precise attribution maps pinpointing the designating patterns forming the natural authenticity of protected areas. Our approach fosters our understanding of the ecological integrity of the protected natural areas and may contribute to future monitoring and preservation efforts.
@ARTICLE{10325539, author={Emam, Ahmed and Stomberg, Timo T. and Roscher, Ribana}, journal={IEEE Geoscience and Remote Sensing Letters}, title={Leveraging Activation Maximization and Generative Adversarial Training to Recognize and Explain Patterns in Natural Areas in Satellite Imagery}, year={2024}, volume={21}, number={}, pages={1-5}, keywords={Adversarial machine learning;Climate change;Biodiversity;Satellite images;Biological system modeling;Climate change;Protected areas;Activation maximization;explainable machine learning;generative models;patterns discovery}, doi={10.1109/LGRS.2023.3335473}}