Unifying Remote Sensing Image Retrieval and Classification with Robust Fine-tuning
This work addresses data scarcity and evaluation inconsistencies in remote sensing image analysis, though it appears incremental as it builds on existing fine-tuning approaches.
The authors tackled the problem of limited annotated data and lack of systematic evaluation in remote sensing image analysis by introducing a new large-scale dataset (SF300) and an adversarial fine-tuning method, achieving improved retrieval and classification performance on nine datasets compared to an ImageNet pretrained baseline.
Advances in high resolution remote sensing image analysis are currently hampered by the difficulty of gathering enough annotated data for training deep learning methods, giving rise to a variety of small datasets and associated dataset-specific methods. Moreover, typical tasks such as classification and retrieval lack a systematic evaluation on standard benchmarks and training datasets, which make it hard to identify durable and generalizable scientific contributions. We aim at unifying remote sensing image retrieval and classification with a new large-scale training and testing dataset, SF300, including both vertical and oblique aerial images and made available to the research community, and an associated fine-tuning method. We additionally propose a new adversarial fine-tuning method for global descriptors. We show that our framework systematically achieves a boost of retrieval and classification performance on nine different datasets compared to an ImageNet pretrained baseline, with currently no other method to compare to.