Change Captioning for Satellite Images Time Series

Satellite images time series (SITS) change detection (CD) provides an efficient way to simultaneously access the temporal and spatial information about the observed region on the Earth. However, the outputs of traditional SITS CD methods, which are either binary maps or semantic change maps, are often difficult to interpret by end users, and conventional remote sensing image change caption methods can only describe bitemporal images. We propose SITS change caption, which not only identifies the changed regions in SITS but also summarizes the changes across SITS in natural language. Unfortunately, the scarcity of available SITS training datasets poses a major challenge for SITS change caption. To address these issues, this letter presents an innovative approach that leverages only bitemporal remote sensing image change caption training data instead of SITS training data for SITS change captioning. Experimental results on real SITS dataset demonstrate the effectiveness of our proposed method, achieving better performance on all indicators. The observed improvements exceeded 20%. The source code can be downloaded from https://github.com/Crueyl123/SITSCC.