Exploring selective image matching methods for zero-shot and few-sample unsupervised domain adaptation of urban canopy prediction
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910412052627456 |
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| author | Francis, John Law, Stephen |
| author_facet | Francis, John Law, Stephen |
| contents | We explore simple methods for adapting a trained multi-task UNet which predicts canopy cover and height to a new geographic setting using remotely sensed data without the need of training a domain-adaptive classifier and extensive fine-tuning. Extending previous research, we followed a selective alignment process to identify similar images in the two geographical domains and then tested an array of data-based unsupervised domain adaptation approaches in a zero-shot setting as well as with a small amount of fine-tuning. We find that the selective aligned data-based image matching methods produce promising results in a zero-shot setting, and even more so with a small amount of fine-tuning. These methods outperform both an untransformed baseline and a popular data-based image-to-image translation model. The best performing methods were pixel distribution adaptation and fourier domain adaptation on the canopy cover and height tasks respectively. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_10626 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Exploring selective image matching methods for zero-shot and few-sample unsupervised domain adaptation of urban canopy prediction Francis, John Law, Stephen Computer Vision and Pattern Recognition Applications We explore simple methods for adapting a trained multi-task UNet which predicts canopy cover and height to a new geographic setting using remotely sensed data without the need of training a domain-adaptive classifier and extensive fine-tuning. Extending previous research, we followed a selective alignment process to identify similar images in the two geographical domains and then tested an array of data-based unsupervised domain adaptation approaches in a zero-shot setting as well as with a small amount of fine-tuning. We find that the selective aligned data-based image matching methods produce promising results in a zero-shot setting, and even more so with a small amount of fine-tuning. These methods outperform both an untransformed baseline and a popular data-based image-to-image translation model. The best performing methods were pixel distribution adaptation and fourier domain adaptation on the canopy cover and height tasks respectively. |
| title | Exploring selective image matching methods for zero-shot and few-sample unsupervised domain adaptation of urban canopy prediction |
| topic | Computer Vision and Pattern Recognition Applications |
| url | https://arxiv.org/abs/2404.10626 |