Unsupervised Multimodal Change Detection Based on Structural Relationship Graph Representation Learning

Hongruixuan Chen

doi:10.1109/TGRS.2022.3229027

2022-01-01

DOI：10.1109/TGRS.2022.3229027

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摘要

En 中文

Unsupervised multimodal change detection is a practical and challenging topic that can play an important role in time-sensitive emergency applications. To address the challenge that multimodal remote sensing images cannot be directly compared due to their modal heterogeneity, we take advantage of two types of modality-independent structural relationships in multimodal images. In particular, we present a structural relationship graph representation learning framework for measuring the similarity of the two structural relationships. First, structural graphs are generated from preprocessed multimodal image pairs by means of an object-based image analysis approach. Then, a structural relationship graph convolutional autoencoder (SR-GCAE) is proposed to learn robust and representative features from graphs. Two loss functions aiming at reconstructing vertex information and edge information are presented to make the learned representations applicable for structural relationship similarity measurement. Subsequently, the similarity levels of two structural relationships are calculated from learned graph representations, and two difference images are generated based on the similarity levels. After obtaining the difference images, an adaptive fusion strategy is presented to fuse the two difference images. Finally, a morphological filtering-based postprocessing approach is employed to refine the detection results. Experimental results on six datasets with different modal combinations demonstrate the effectiveness of the proposed method.

Keyword:

Change detection

graph convolutional autoencoder

graph representation learning

multimodal remote sensing images

structural relationship

期刊

IEEE Transactions on Geoscience and Remote Sensing

IF:

8.6

论文数:

2.1W

被引数:

10.7W

机构

University of Tokyo

学者数:

7.1W

论文数: 6.5W

被引数: 2.2K

wuhan university

学者数:

8.1W

论文数: 5.8W

被引数: 70

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