CCST-KFN: a novel unsupervised urban vegetation change detection approach for high-resolution RGB remote sensing imagery

Accurate detection of urban vegetation changes is essential for maintaining ecological balance and promoting sustainable urban development. Deep learning has become a leading technology for urban vegetation change detection, owing to its superior performance in feature representation and detection accuracy. High-resolution RGB remote sensing imagery has emerged as the key data source for deep learning-based urban vegetation change detection, due to its fine spatial detail and lower acquisition cost compared to high-resolution multispectral imagery. However, most of the existing urban vegetation change detection methods based on deep learning are supervised and require manually annotated training samples, limiting their automation and efficiency. In this study, we proposed a novel unsupervised urban vegetation change detection approach (CCST-KFN) for high-resolution RGB remote sensing imagery. Specifically, an advanced cross-scale change sample transfer (CCST) method was developed to automatically generate reliable high-resolution training samples. It first produces medium-resolution urban vegetation changed and unchanged samples by pre-detection, leveraging the more discriminative spectral features available in medium-resolution multispectral imagery. These samples are then mapped to the high-resolution domain guided by change information. In addition, a dedicated urban vegetation change detection network by fusing domain knowledge and change features (KFN) was designed for urban vegetation change mapping. It takes as input two high-resolution RGB images and two resampled enhanced normalized difference vegetation index images, which emphasize urban vegetation by combining near-infrared, red, and blue bands. This enables the network to effectively exploit the comprehensive features and domain knowledge of urban vegetation changes. Experiments on three typical urban datasets demonstrate that the proposed CCST-KFN identified urban vegetation changes more completely and with fewer errors compared to seven unsupervised change detection methods. Moreover, its application to a representative urban area in Nanjing City, China, further validates its practical effectiveness. Overall, the proposed CCST-KFN offers a reliable and automated solution for accurate urban vegetation change detection using high-resolution RGB remote sensing imagery, with the potential to further support urban ecological security and sustainable development.