Efficient and accurate satellite image analysis: a magnifying network (MagNet) approach

In this paper we investigate the potential of Magnifying Networks, an architecture recently proposed in the domain of digital pathology, in the realm of satellite imagery analysis, an increasingly important remote sensing modality of practical significance in a wide range of domains. In particular, we address the challenges posed by the extreme size of satellite images and the inadequacy of the current state of the art in capturing salient information across scales while remaining computationally feasible. Specifically, we adapt the MagNet architecture by adjusting the number of magnifying layers and by employing the softmax function as a specific mechanistic means in the exploratory search within each magnifying layer. In addition, we conduct a series of comparative experiments to identify effective design choices and their effect on performance in the specific context of satellite remote sensing. Focusing on the most challenging classes in the Functional Map of the World with small object-to-image ratios, the adapted MagNet surpasses an Inception-v3 baseline (AUROC 0.89 vs. 0.85, Accuracy 0.83 vs. 0.71, F1-score 0.84 vs. 0.76), supporting adaptive magnification as an effective modelling approach to gigapixel satellite imagery.