对比7种深度学习与传统方法在多时相卫星影像匹配中的表现
Deep Learning Meets Satellite Images -- An Evaluation on Handcrafted and Learning-based Features for Multi-date Satellite Stereo Images
- 采用500对遥感立体像对，测试10种特征匹配算法
- SIFT等经典方法在多数场景下仍优于深度学习模型
- 适合遥感影像处理、数字地表建模的研究者参考
生成数字地表模型（DSM）的关键步骤是特征匹配。特别是对于非同步（或跨时相）卫星立体影像，由于影像间光谱畸变、长基线和大交角，特征匹配性能受到挑战。特征匹配方法已从传统的手工设计方法（如SIFT）发展到基于深度学习的方法（如SuperPoint和SuperGlue）。本文系统比较了多种特征提取与匹配方法在卫星影像上的表现。实验使用约500对覆盖两个不同区域的立体像对，对比了SIFT与七种深度学习匹配方法：SuperGlue、LightGlue、LoFTR、ASpanFormer、DKM、GIM-LightGlue和GIM-DKM。结果表明，尽管某些特定场景下学习型方法表现优异，但传统方法在当前阶段依然具有竞争力。
原文摘要 · Abstract (English)
A critical step in the digital surface models(DSM) generation is feature matching. Off-track (or multi-date) satellite stereo images, in particular, can challenge the performance of feature matching due to spectral distortions between images, long baseline, and wide intersection angles. Feature matching methods have evolved over the years from handcrafted methods (e.g., SIFT) to learning-based methods (e.g., SuperPoint and SuperGlue). In this paper, we compare the performance of different features, also known as feature extraction and matching methods, applied to satellite imagery. A wide range of stereo pairs(~500) covering two separate study sites are used. SIFT, as a widely used classic feature extraction and matching algorithm, is compared with seven deep-learning matching methods: SuperGlue, LightGlue, LoFTR, ASpanFormer, DKM, GIM-LightGlue, and GIM-DKM. Results demonstrate that traditional matching methods are still competitive in this age of deep learning, although for particular scenarios learning-based methods are very promising.
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