Exchange means change: an unsupervised single-temporal change detection
  framework based on intra- and inter-image patch exchange
Hongruixuan Chen Jian Song Chen Wu Bo Du Naoto Yokoya
Change detection (CD) is a critical task in studying the dynamics of
ecosystems and human activities using multi-temporal remote sensing images.
While deep learning has shown promising results in CD tasks, it requires a
large number of labeled and paired multi-temporal images to achieve high
performance. Pairing and annotating large-scale multi-temporal remote sensing
images is both expensive and time-consuming. To make deep learning-based CD
techniques more practical and cost-effective, we propose an unsupervised
single-temporal CD framework based on intra- and inter-image patch exchange
(I3PE). The I3PE framework allows for training deep change detectors on
unpaired and unlabeled single-temporal remote sensing images that are readily
available in real-world applications. The I3PE framework comprises four steps:
1) intra-image patch exchange method is based on an object-based image analysis
method and adaptive clustering algorithm, which generates pseudo-bi-temporal
image pairs and corresponding change labels from single-temporal images by
exchanging patches within the image; 2) inter-image patch exchange method can
generate more types of land-cover changes by exchanging patches between images;
3) a simulation pipeline consisting of several image enhancement methods is
proposed to simulate the radiometric difference between pre- and post-event
images caused by different imaging conditions in real situations; 4)
self-supervised learning based on pseudo-labels is applied to further improve
the performance of the change detectors in both unsupervised and
semi-supervised cases. Extensive experiments on two large-scale datasets
demonstrate that I3PE outperforms representative unsupervised approaches and
achieves F1 value improvements of 10.65% and 6.99% to the SOTA method.
Moreover, I3PE can improve the performance of the ... (see the original article
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