Graph Information Bottleneck for Remote Sensing Segmentation

TL;DR

This paper introduces a novel graph-based contrastive learning approach for remote sensing segmentation, leveraging information bottleneck theory to enhance task-specific feature learning and improve segmentation accuracy.

Contribution

It proposes a simple contrastive vision GNN architecture with adaptive masking and integrates information bottleneck theory into graph contrastive learning for the first time.

Findings

Outperforms state-of-the-art segmentation methods on real datasets.

Effectively learns task-related features while reducing redundant information.

Demonstrates robustness across different remote sensing datasets.

Abstract

Remote sensing segmentation has a wide range of applications in environmental protection, and urban change detection, etc. Despite the success of deep learning-based remote sensing segmentation methods (e.g., CNN and Transformer), they are not flexible enough to model irregular objects. In addition, existing graph contrastive learning methods usually adopt the way of maximizing mutual information to keep the node representations consistent between different graph views, which may cause the model to learn task-independent redundant information. To tackle the above problems, this paper treats images as graph structures and introduces a simple contrastive vision GNN (SC-ViG) architecture for remote sensing segmentation. Specifically, we construct a node-masked and edge-masked graph view to obtain an optimal graph structure representation, which can adaptively learn whether to mask nodes and edges. Furthermore, this paper innovatively introduces information bottleneck theory into graph contrastive learning to maximize task-related information while minimizing task-independent redundant information. Finally, we replace the convolutional module in UNet with the SC-ViG module to complete the segmentation and classification tasks of remote sensing images. Extensive experiments on publicly available real datasets demonstrate that our method outperforms state-of-the-art remote sensing image segmentation methods.