Exchange means change: an unsupervised single-temporal change detection framework based on intra- and inter-image patch exchange

• URL: http://arxiv.org/abs/2310.00689v1
• Date: Sun, 1 Oct 2023 14:50:54 GMT
• Title: Exchange means change: an unsupervised single-temporal change detection framework based on intra- and inter-image patch exchange
• Authors: Hongruixuan Chen and Jian Song and Chen Wu and Bo Du and Naoto Yokoya
• Abstract summary: 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. I3PE outperforms representative unsupervised approaches and achieves F1 value improvements of 10.65% and 6.99% to the SOTA method.
• Score: 44.845959222180866
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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 for full abstract)

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