Feature-Differencing-Based Self-Supervised Pre-Training for Land-Use/Land-Cover Change Detection in High-Resolution Remote Sensing Images

Wenqing Feng (), Fangli Guan, Chenhao Sun and Wei Xu
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Wenqing Feng: Computer & Software School, Hangzhou Dianzi University, Hangzhou 310018, China
Fangli Guan: Computer & Software School, Hangzhou Dianzi University, Hangzhou 310018, China
Chenhao Sun: Electrical & Information Engineering School, Changsha University of Science & Technology, Changsha 410114, China
Wei Xu: Information System and Management College, National University of Defense Technology, Changsha 410015, China

Land, 2024, vol. 13, issue 7, 1-20

Abstract: Land-use and land-cover (LULC) change detection (CD) is a pivotal research area in remote sensing applications, posing a significant challenge due to variations in illumination, radiation, and image noise between bi-temporal images. Currently, deep learning solutions, particularly convolutional neural networks (CNNs), represent the state of the art (SOTA) for CD. However, CNN-based models require substantial amounts of annotated data, which can be both expensive and time-consuming. Conversely, acquiring a large volume of unannotated images is relatively easy. Recently, self-supervised contrastive learning has emerged as a promising method for learning from unannotated images, thereby reducing the need for annotation. However, most existing methods employ random values or ImageNet pre-trained models to initialize their encoders and lack prior knowledge tailored to the demands of CD tasks, thus constraining the performance of CD models. To address these challenges, we introduce a novel feature-differencing-based framework called Barlow Twins for self-supervised pre-training and fine-tuning in CD (BTCD). The proposed approach employs absolute feature differences to directly learn unique representations associated with regions that have changed from unlabeled bi-temporal remote sensing images in a self-supervised manner. Moreover, we introduce invariant prediction loss and change consistency regularization loss to enhance image alignment between bi-temporal images in both the decision and feature space during network training, thereby mitigating the impact of variation in radiation conditions, noise, and imaging viewpoints. We select the improved UNet++ model for fine-tuning self-supervised pre-training models and conduct experiments using two publicly available LULC CD datasets. The experimental results demonstrate that our proposed approach outperforms existing SOTA methods in terms of competitive quantitative and qualitative performance metrics.

Keywords: Barlow Twins loss function; land use/land cover change detection; pre-training; self-supervised contrastive learning (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
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