Deep learning interpretability for understanding forest disturbance driver classification from Sentinel-1 and -2 data

Abstract

Monitoring the drivers of tropical forest disturbances using remote-sensing data has become increasingly critical to supporting actionable law enforcement and sustainable land management. Using information captured by multi-source Earth Observation data, deep learning-based fusion models are state-of-the-art in many remote sensing applications. Despite their efficacy, the inherent black-box nature of these deep neural networks poses challenges to our understanding of their decision-making processes. To enhance their interpretability, we applied eXplainable Artificial Intelligence (XAI) methods for several deep learning-based models including single and multi-modal approaches. We evaluated six XAI methods: Integrated Gradients, GradientShap, Saliency, Deconvolution, Guided Grad-CAM, and Guided Backpropagation. Using both quantitative and qualitative assessments, we conducted extensive experiments to evaluate the capability of each XAI method to interpret the proposed models. Our analysis included variable importance, single- and multi-class explanations, cloud cover analysis, and instances of misclassification. We identified Guided Grad-CAM as the most reliable of these methods. In addition, we gained deeper insight into how positive and negative attribution scores influence the interpretation of model output, highlighting the need for more research on the significance of negative values. Our study improves the understanding of deep learning model decisions in the context of forest disturbance driver classification, shedding light on the interpretability of fusion models and dataset characteristics. It establishes a connection between remote sensing applications and XAI methodologies. This work was supported by the Open Domain Science project Forest Carbon Crime under Grant OCENW.M.21.203; Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO); Norway’s Climate and Forest Initiative (NICFI) and World Wide Fund for Nature (WWF) the Netherlands.