Confidence Estimation in Unsupervised Deep Change Vector Analysis
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arXiv
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866929345554022400 |
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| author | Saha, Sudipan |
| author_facet | Saha, Sudipan |
| contents | Unsupervised transfer learning-based change detection methods exploit the feature extraction capability of pre-trained networks to distinguish changed pixels from the unchanged ones. However, their performance may vary significantly depending on several geographical and model-related aspects. In many applications, it is of utmost importance to provide trustworthy or confident results, even if over a subset of pixels. The core challenge in this problem is to identify changed pixels and confident pixels in an unsupervised manner. To address this, we propose a two-network model - one tasked with mere change detection and the other with confidence estimation. While the change detection network can be used in conjunction with popular transfer learning-based change detection methods such as Deep Change Vector Analysis, the confidence estimation network operates similarly to a randomized smoothing model. By ingesting ensembles of inputs perturbed by noise, it creates a distribution over the output and assigns confidence to each pixel's outcome. We tested the proposed method on three different Earth observation sensors: optical, Synthetic Aperture Radar, and hyperspectral sensors. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_09896 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Confidence Estimation in Unsupervised Deep Change Vector Analysis Saha, Sudipan Image and Video Processing Unsupervised transfer learning-based change detection methods exploit the feature extraction capability of pre-trained networks to distinguish changed pixels from the unchanged ones. However, their performance may vary significantly depending on several geographical and model-related aspects. In many applications, it is of utmost importance to provide trustworthy or confident results, even if over a subset of pixels. The core challenge in this problem is to identify changed pixels and confident pixels in an unsupervised manner. To address this, we propose a two-network model - one tasked with mere change detection and the other with confidence estimation. While the change detection network can be used in conjunction with popular transfer learning-based change detection methods such as Deep Change Vector Analysis, the confidence estimation network operates similarly to a randomized smoothing model. By ingesting ensembles of inputs perturbed by noise, it creates a distribution over the output and assigns confidence to each pixel's outcome. We tested the proposed method on three different Earth observation sensors: optical, Synthetic Aperture Radar, and hyperspectral sensors. |
| title | Confidence Estimation in Unsupervised Deep Change Vector Analysis |
| topic | Image and Video Processing |
| url | https://arxiv.org/abs/2405.09896 |