SatSynth: Augmenting Image-Mask Pairs through Diffusion Models for Aerial Semantic Segmentation (2403.16605v1)

Abstract: In recent years, semantic segmentation has become a pivotal tool in processing and interpreting satellite imagery. Yet, a prevalent limitation of supervised learning techniques remains the need for extensive manual annotations by experts. In this work, we explore the potential of generative image diffusion to address the scarcity of annotated data in earth observation tasks. The main idea is to learn the joint data manifold of images and labels, leveraging recent advancements in denoising diffusion probabilistic models. To the best of our knowledge, we are the first to generate both images and corresponding masks for satellite segmentation. We find that the obtained pairs not only display high quality in fine-scale features but also ensure a wide sampling diversity. Both aspects are crucial for earth observation data, where semantic classes can vary severely in scale and occurrence frequency. We employ the novel data instances for downstream segmentation, as a form of data augmentation. In our experiments, we provide comparisons to prior works based on discriminative diffusion models or GANs. We demonstrate that integrating generated samples yields significant quantitative improvements for satellite semantic segmentation -- both compared to baselines and when training only on the original data.

• The paper introduces a novel DDPM framework that synthesizes paired satellite images and segmentation masks to augment scarce datasets.
• It demonstrates significant improvements in semantic segmentation across benchmarks like iSAID, LoveDA, and OpenEarthMap.
• The method offers a scalable solution for generating high-quality annotated data, benefiting remote sensing and related applications.

Augmenting Aerial Imagery Datasets with Synthetic Image-Mask Pairs via Diffusion Models for Semantic Segmentation

Introduction

The surge in availability and resolution of satellite imagery has ushered in a golden age for Earth observation, enabling advances in numerous humanitarian and environmental sectors. However, the paucity of corresponding annotated data remains a substantial bottleneck for leveraging the power of machine learning in this domain. In this paper titled "SatSynth: Augmenting Image-Mask Pairs through Diffusion Models for Aerial Semantic Segmentation," we explore the viability of denoising diffusion probabilistic models (DDPM) for generating synthetic satellite imagery with corresponding semantic labels, aimed at augmenting existing datasets. This approach serves to explore data synthesis where annotated data are scarce and expensive to produce, addressing a fundamental challenge in supervised learning for satellite imagery analysis.

Methodology

The authors propose a novel framework that utilizes DDPM to jointly generate paired satellite images and their corresponding semantic segmentation masks. This is achieved by learning the joint distribution of images and labels in a bit-space formulation, allowing for the synthesis of additional, diverse training instances. The core contributions include:

1. Learning the joint data distribution of images and labels via a diffusion model, thereby enabling the synthesis of novel training data instances for data augmentation.
2. Demonstrating significant improvements in semantic segmentation tasks on satellite images by incorporating the synthetic data instances into the training process.
3. Offering a comprehensive evaluation of the proposed method against existing benchmarks, thereby establishing its effectiveness.

Extensive experiments on three satellite imagery benchmarks highlight the method's capability to generate high-quality, diverse synthetic image-mask pairs that, when used in conjunction with original dataset instances, lead to marked improvements in semantic segmentation performance.

Experiments and Results

The experimentations underscore the utility of the generated synthetic pairs for enhancing semantic segmentation models. When evaluated across multiple datasets such as iSAID, LoveDA, and OpenEarthMap, the inclusion of synthesized samples yielded notable quantitative improvements. The framework demonstrated superiority over baseline methods, including GAN-based and discriminative diffusion models previously applied to such tasks.

A noteworthy result was the consistent increase in segmentation performance across different baseline segmentation models when trained on the combined original and synthesized data, as opposed to training solely on the original dataset. This evidences the synthetic data's quality and its effectiveness in diversifying the training set. Furthermore, the proposed method's ability to significantly improve object-centric segmentation metrics on iSAID, a dataset with pronounced class imbalances and scale variation challenges, emphasizes the synthetic data's applicability to complex segmentation scenarios.

Theoretical Implications

This work suggests several promising directions for future research. The approach underscores the potential of DDPM in effectively learning joint image-label distributions, a relatively unexplored territory in the field of aerial image analysis. The technique's success in generating synthetic data that can pass as real—enough to improve downstream task performance—points to an avenue for generating datasets where manual annotation is impractical.

Practical Applications

Beyond academic interest, this research has direct implications for remote sensing, urban planning, environmental monitoring, and more, by substantially reducing the bottleneck of annotated data scarcity. The ability to synthesize realistic, diverse training data could accelerate the development of more accurate models for land use classification, disaster response, and other critical applications.

Conclusion

"SatSynth" presents a compelling case for the role of DDPM in addressing the data scarcity challenge in the domain of satellite image analysis. By generating high-quality, labeled synthetic data, this approach offers a scalable solution to enhance the performance of semantic segmentation models. The method's success across multiple benchmarks and its clear improvements over existing methods mark it as a significant step forward in the ongoing effort to leverage the full potential of satellite imagery for Earth observation.