Scale-Aware Recognition in Satellite Images under Resource Constraints
This addresses the challenge of efficient satellite image analysis for applications like environmental monitoring, though it is incremental in combining existing techniques.
The paper tackled the problem of recognizing features in satellite imagery under resource constraints by determining optimal resolutions and acquisition strategies, resulting in a 26.3% improvement in accuracy while using 76.3% fewer high-resolution images.
Recognition of features in satellite imagery (forests, swimming pools, etc.) depends strongly on the spatial scale of the concept and therefore the resolution of the images. This poses two challenges: Which resolution is best suited for recognizing a given concept, and where and when should the costlier higher-resolution (HR) imagery be acquired? We present a novel scheme to address these challenges by introducing three components: (1) A technique to distill knowledge from models trained on HR imagery to recognition models that operate on imagery of lower resolution (LR), (2) a sampling strategy for HR imagery based on model disagreement, and (3) an LLM-based approach for inferring concept "scale". With these components we present a system to efficiently perform scale-aware recognition in satellite imagery, improving accuracy over single-scale inference while following budget constraints. Our novel approach offers up to a 26.3% improvement over entirely HR baselines, using 76.3% fewer HR images.