Tiny Objects Classification on Remote Sensing Image by Using Multi-Scale Crop

Classifying tiny objects in remote sensing images (e.g., a 20x20 pixels target within a resolution 1000x1000 image) is a significant challenge. This paper adopts a fused FPN (Feature Pyramid Network) to enhance the feature extraction capability for tiny objects. Additionally, the proposed multi-scale crop method can more effectively focus on the target objects. This network architecture improves overall accuracy by 14.4%.