SUES-200: A Multi-height Multi-scene Cross-view Image Benchmark Across Drone and Satellite

TL;DR

SUES-200 is a novel dataset comprising drone and satellite images at multiple heights and scenes, designed to evaluate and improve cross-view image matching models for drone navigation in complex environments.

Contribution

The paper introduces SUES-200, the first public dataset with multi-height drone images and diverse scenes, along with an evaluation framework and a robust baseline model.

Findings

SUES-200 enables models to learn height-discriminative features.

Nine architectures were comprehensively evaluated on SUES-200.

The baseline model demonstrates improved cross-view matching performance.

Abstract

Cross-view image matching aims to match images of the same target scene acquired from different platforms. With the rapid development of drone technology, cross-view matching by neural network models has been a widely accepted choice for drone position or navigation. However, existing public datasets do not include images obtained by drones at different heights, and the types of scenes are relatively homogeneous, which yields issues in assessing a model's capability to adapt to complex and changing scenes. In this end, we present a new cross-view dataset called SUES-200 to address these issues. SUES-200 contains 24120 images acquired by the drone at four different heights and corresponding satellite view images of the same target scene. To the best of our knowledge, SUES-200 is the first public dataset that considers the differences generated in aerial photography captured by drones flying at different heights. In addition, we developed an evaluation for efficient training, testing and evaluation of cross-view matching models, under which we comprehensively analyze the performance of nine architectures. Then, we propose a robust baseline model for use with SUES-200. Experimental results show that SUES-200 can help the model to learn highly discriminative features of the height of the drone.