OCA: Object-Based Change Augmentation for Few-Shot Building Change Detection in Very High-Resolution Remote Sensing Images

Change detection (CD) based on multi-temporal remote sensing imagery is a crucial step for various earth observation applications. While deep learning (DL) has revolutionized CD, its data-driven nature demands substantial labeled images for supervised model training, which is costly and time-consuming. This paper addresses the challenge of limited training samples by proposing a novel object-based change augmentation (OCA) method. Unlike conventional image-level augmentation methods that can introduce irrelevant contextual dependencies, OCA decomposes the augmentation process into few-shot object classification and foreground-background pasting, thereby generating in-distribution synthetic images with increased change diversity. An object-based training strategy is developed to create a high-confidence binary classifier for pseudo-semantic segmentation, facilitating the copy-paste operation. Experimental results on the very high-resolution remote sensing images demonstrate the superior performance of OCA compared to existing augmentation-based and generation-based methods. A comprehensive analysis of parameter sensitivity, adaptability to varying training data volumes, and compatibility with diverse CD methods validate its robustness. This approach provides a practical and effective solution for few-shot CD scenarios, advancing the applicability of DL-based CD methods in training data-limited environments. Codes and data are available: https://github.com/openrsgis/OCA.