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NF-SemiCD: semi-supervised remote sensing change detection with normalizing flows
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DOI：10.7717/peerj-cs.3751.png)
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Abstract
En 中文
Change detection (CD) in remote sensing images has long been of great interest to researchers. Due to the substantial time and effort required for data annotation in remote sensing, semi-supervised methods have attracted extensive attention as a promising solution for achieving satisfactory performance under limited samples. However, existing semi-supervised approaches often encounter significant challenges, most notably class imbalance and subtle changes in feature distributions, making it difficult to distinguish changes from unchanged regions. Here, we propose a novel strategy, named NF-SemiCD, which incorporates normalizing flows in a semi-supervised architecture. We leverage normalizing flows to characterize the feature distribution of unchanged regions and derive a probability distribution model. Its sensitivity to changes in probability allows us to get a probability feature map, which provides useful information on deep difference features. To this end, we devise a three-stage training scheme: (1) training an encoder-decoder network with labeled data, (2) training a normalizing flow decoder on labeled data, and (3) training the encoder-decoder network with all data. Experiments on three benchmark datasets demonstrate that NF-SemiCD outperforms existing state-of-the-art methods, highlighting its potential for improved change detection under semi-supervised settings.
Keywords:
Change detection (CD)
Remote sensing
Semi-supervised learning (SSL)
Normalizing flows
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