JointNet4BCD: A Semi-Supervised Joint Learning Neural Network with Decision Fusion for Building Change Detection
2024
Hao Chen | Chengzhe Sun | Jun Li | Chun Du
Remote sensing image building change detection aims to identify building changes that occur in remote sensing images of the same areas acquired at different times. In recent years, the development of deep learning has led to significant advancements in building change detection methods. However, these fully supervised methods require a large number of bi-temporal remote sensing images with pixel-wise change detection labels to train the model, which incurs substantial time and manpower for annotation. To address this issue, this study proposes a novel single-temporal semi-supervised joint learning framework for building change detection, called JointNet4BCD. Firstly, to reduce annotation costs, we design a semi-supervised learning manner to train our model using a small number of building extraction labels instead of a large amount of building change detection labels. Furthermore, to improve the semantic understanding capability of the model, we propose a joint learning approach for building extraction and change detection tasks. Lastly, a decision fusion block is designed to fuse the building extraction results into the building change detection results to further improve the accuracy of building change detection. Experimental results on the two widely used datasets demonstrate that the proposed JointNet4BCD achieves excellent building change detection performance while reducing the need for labels from thousands to dozens. Using only ten labeled images, JointNet4BCD achieved F1-Scores of 83.93% and 83.45% on the LEVIR2000 and WHU datasets, respectively.
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