Unifying Remote Sensing Image Retrieval and Classification with Robust Fine-tuning

• URL: http://arxiv.org/abs/2102.13392v1
• Date: Fri, 26 Feb 2021 11:01:30 GMT
• Title: Unifying Remote Sensing Image Retrieval and Classification with Robust Fine-tuning
• Authors: Dimitri Gominski, Val\'erie Gouet-Brunet, Liming Chen
• Abstract summary: We aim at unifying remote sensing image retrieval and classification with a new large-scale training and testing dataset, SF300. We show that our framework systematically achieves a boost of retrieval and classification performance on nine different datasets compared to an ImageNet pretrained baseline.
• Score: 3.6526118822907594
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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