Detecting Unusual Earth Observation Images Using Diffusion Models

Core Concepts

Diffusion models can effectively serve as unsupervised out-of-distribution detectors for remote sensing images, using their reconstruction error as a plausibility score. The introduced ODEED scorer leveraging the probability-flow ODE of diffusion models significantly outperforms other diffusion-based and discriminative baselines on challenging near-OOD scenarios like flood image detection.

Abstract

This paper investigates the use of diffusion models for out-of-distribution (OOD) detection in remote sensing imagery. The authors demonstrate that the reconstruction error of diffusion models can be used as an unsupervised OOD detector, and introduce a novel scorer called ODEED that leverages the probability-flow ODE of diffusion models.
The authors experiment with three scenarios on the SpaceNet 8 dataset:

Detecting pre-flood vs. post-flood images within the same geographical domain.
Discriminating flooded vs. non-flooded areas in post-event images.
Identifying a geographical domain shift between East Louisiana and Germany.

The results show that ODEED significantly outperforms other diffusion-based and discriminative OOD detection baselines, especially on the more challenging near-OOD scenarios like flood image detection. The authors also analyze the impact of the corruption time t0 on the OOD detection performance and find that smaller t0 values are better suited for localized anomalies, while intermediate t0 works better for global distribution shifts.
Furthermore, the authors evaluate the cross-domain generalization of diffusion models and find that their OOD detection abilities are preserved when using a model trained on a different geographical domain. This suggests that diffusion models can be effective for detecting anomalies even under non-stationary data distributions.
The paper aims to pave the way towards better use of generative models for anomaly detection in remote sensing, highlighting the potential of diffusion models for this task.

Stats

Diffusion models trained on pre-event images from the SpaceNet 8 dataset can effectively detect post-flood images, achieving an AUC of 87.9% and FPR95% of 20.5% on the Germany subset.
For the non-flooded/flooded scenario, ODEED with MSE metric achieves an AUC of 83.6% and FPR95% of 33.3% on the Germany subset.
On the domain OOD task, one-step denoising with LPIPS metric reaches an AUC of 82.1% and FPR95% of 20.9% on the Louisiana subset.

Quotes

"Diffusion models proved their synthesis capabilities not only for images but also for video, audio, text, and protein design."
"Diffusion models have outclassed previous models in image generation, such as Generative Adversarial Networks, due to their strong ability to model complex distributions without mode collapse."
"Being able to detect these OOD images is helpful to avoid considering degraded model predictions. In addition, disasters such as floods, forest fires, and storms are also unfrequent and catastrophic events, that are rarely observed in remote sensing datasets."

Key Insights Distilled From

Detecting Out-Of-Distribution Earth Observation Images with Diffusion Models

by Georges Le B... at arxiv.org 04-22-2024

https://arxiv.org/pdf/2404.12667.pdf

Deeper Inquiries

How could the ODEED scorer be extended to handle conditional diffusion models, which could leverage additional information like segmentation masks or textual descriptions to improve OOD detection

The ODEED scorer could be extended to handle conditional diffusion models by incorporating additional information such as segmentation masks or textual descriptions. This extension would involve conditioning the diffusion model on the extra information during training and inference. During training, the model would learn to generate latent representations that are conditioned on the segmentation masks or textual descriptions. This conditioning would enable the model to capture more complex relationships between the input data and the additional information, leading to improved OOD detection performance.
At inference time, the ODEED scorer would encode the input image along with the segmentation mask or textual description into a latent representation using the PF-ODE. By decoding this latent representation, the model would reconstruct the input image while taking into account the additional information. The reconstruction error between the original image and the reconstructed image would then be used as the OOD score. This approach would allow the model to leverage the additional information to better discriminate between in-distribution and out-of-distribution samples, leading to more accurate OOD detection in remote sensing imagery.

What other types of anomalies or rare events in remote sensing imagery could be detected using diffusion models beyond the flood and geographical shift scenarios explored in this work

Beyond the flood and geographical shift scenarios explored in this work, diffusion models could be used to detect various other types of anomalies or rare events in remote sensing imagery. Some potential applications include:

Fire Detection: Diffusion models could be trained to detect areas affected by wildfires by learning the visual patterns associated with burnt areas. The models could reconstruct images and identify anomalies in vegetation cover indicative of fire damage.

Urban Development Monitoring: Diffusion models could be used to detect changes in urban landscapes over time, such as new construction, infrastructure development, or changes in land use. By comparing pre-event and post-event images, the models could identify areas undergoing significant changes.

Natural Disaster Detection: Apart from floods, diffusion models could be applied to detect other natural disasters like landslides, earthquakes, or storms. By training on historical data and monitoring changes in the landscape, the models could flag areas affected by such events.

Vegetation Health Monitoring: Diffusion models could be used to detect anomalies in vegetation health, such as disease outbreaks or pest infestations. By analyzing changes in vegetation cover and health indicators, the models could identify areas requiring attention.

Environmental Pollution Detection: Diffusion models could be employed to detect environmental pollution in remote sensing imagery, such as oil spills, chemical leaks, or air pollution. By analyzing visual cues associated with pollution, the models could identify contaminated areas.

By adapting the ODEED scorer and training the diffusion models on relevant datasets, these applications could benefit from the anomaly detection capabilities of diffusion models in remote sensing imagery.

Could the insights from this work on the impact of the corruption time t0 be leveraged to develop adaptive OOD detection methods that dynamically adjust the corruption level based on the type of anomaly being detected

The insights from this work on the impact of the corruption time t0 could be leveraged to develop adaptive OOD detection methods that dynamically adjust the corruption level based on the type of anomaly being detected. By understanding how different corruption levels affect the reconstruction error and OOD detection performance, adaptive methods could optimize the corruption process for specific anomaly types. Here are some ways this insight could be applied:

Dynamic Corruption Level: The adaptive method could analyze the characteristics of the anomaly being detected and adjust the corruption level (t0) accordingly. For anomalies that require fine-grained details for detection, a lower corruption level could be used, while anomalies with broader visual cues could benefit from a higher corruption level.

Anomaly-Specific Tuning: By categorizing anomalies based on their visual features or complexity, the adaptive method could predefine optimal corruption levels for different anomaly types. This would streamline the OOD detection process and improve detection accuracy for specific anomaly categories.

Feedback Mechanism: The adaptive method could incorporate a feedback mechanism that evaluates the OOD detection performance based on the corruption level used. By analyzing the reconstruction errors and detection outcomes, the method could iteratively adjust the corruption level to optimize OOD detection for different anomaly scenarios.

Machine Learning Integration: The adaptive method could be integrated with machine learning algorithms that learn the optimal corruption level for specific anomaly types over time. By leveraging feedback loops and continuous learning, the method could continuously improve OOD detection performance across a wide range of anomaly categories.

By implementing these adaptive strategies based on the insights from the impact of the corruption time t0, OOD detection methods using diffusion models could become more versatile, efficient, and effective in detecting various anomalies in remote sensing imagery.