Summary of Detecting Out-of-distribution Earth Observation Images with Diffusion Models, by Georges Le Bellier (cedric – Vertigo et al.
Detecting Out-Of-Distribution Earth Observation Images with Diffusion Models
by Georges Le Bellier, Nicolas Audebert
First submitted to arxiv on: 19 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes an innovative approach to detecting rare and unusual events in Earth Observation (EO) imagery using deep learning-based methods. The authors leverage the reconstruction error of diffusion models as a plausibility score to identify out-of-distribution (OOD) samples, which can anticipate changes in observations. They introduce ODEED, a novel scorer that utilizes the probability-flow ODE of diffusion models. Experimenting with SpaceNet 8 and various scenarios, such as geographical shift and near-OOD setups like pre/post-flood image recognition, the authors demonstrate that ODEED significantly outperforms other baselines in detecting flood images, which are close to the distribution tail. This work paves the way for better using generative models for anomaly detection in remote sensing. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research helps computers better recognize unusual events in Earth Observation imagery, like natural disasters or big changes in landscapes. The team uses a special type of computer model called a diffusion model to identify these rare events. They show that this model can be used as a score to measure how likely an image is to be unusual. In experiments, they tested their approach on images from different times and places, including before and after floods. Their method performed well in detecting flood images, which are similar but not identical to usual images. This work helps us use computer models more effectively for recognizing unusual events in Earth Observation imagery. |
Keywords
» Artificial intelligence » Anomaly detection » Deep learning » Diffusion model » Probability
