A Simple and Automatic Method for Detecting Large-Scale Land Cover Changes Without Training Data

Automatic and accurate extraction of land use/cover change (LUCC) is essential for various applications, particularly studies on climate change and sustainable development. However, the automatic and accurate detection of LUCC in large-scale regions remains challenging due to the complexity of LUCC and the high cost of training data acquisition. To address this research gap, a simple and practical unsupervised change detection method based on multi-indices and bitemporal remote sensing image pairs (USCD-MiBi) was proposed to automatically extract LUCC in two heterogeneous experimental sites without training data. Single-index change analysis (SICA), multi-index change analysis (MICA), and bitemporal change analysis (BTCA) form the core of the USCD-MiBi method. In this method, all the selected change indices were integrated by the SICA and MICA to identify potential change regions. The potential false changes, caused by inconsistencies in atmospheric conditions and phenology, were further removed by the BTCA. Verification experiments revealed that the detection accuracy of USCD-MiBi method can exceed 90%, whether the LUCC is concentrated in urban regions or scattered in mountainous areas. It was also observed that the proposed method had higher detection performance than several other change detection methods. The proposed USCD-MiBi method offers high flexibility and adaptability, allowing users to choose the most suitable change indices based on the characteristics of their study area or the selected data sources. This study provides a simple solution for automatically detecting large-scale LUCC without training samples, making it accessible to a wider range of users.

Automated summary

This study proposes a simple and practical unsupervised change detection method based on multi-indices and bitemporal remote sensing image pairs (USCD-MiBi) to automatically extract land use/cover change (LUCC). The method integrates single-index change analysis, multi-index change analysis, and bitemporal change analysis to identify potential change regions and remove false changes caused by atmospheric conditions and phenology inconsistencies. The USCD-MiBi method demonstrates high accuracy in detecting LUCC in different regions.