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

Abstract

Change detection 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 change detection 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 change detection techniques more practical and cost-effective, we propose an unsupervised single-temporal change detection 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 (OBIA) 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 covering Hong Kong, Shanghai, Hangzhou, and Chengdu, China, demonstrate that I3PE outperforms representative unsupervised approaches and achieves F1 value improvements of 10.65% and 6.99% to the state-of-the-art method. Moreover, I3PE can improve the performance of the change detector by 4.37% and 2.61% on F1 values in the case of semi-supervised settings. Additional experiments on a dataset covering a study area with 144 km2 in Wuhan, China, confirm the effectiveness of I3PE for practical land-cover change analysis tasks.