Pixel-Level Change Detection Pseudo-Label Learning For Remote Sensing Change Captioning
Chenyang Liu, Keyan Chen, Zipeng Qi, Zili Liu, Haotian Zhang, Zhengxia Zou, Zhenwei Shi
Abstract
The existing Remote Sensing Image Change Captioning (RSICC) methods perform well in simple scenes but exhibit poorer performance in complex scenes. This limitation is primarily attributed to the model’s constrained visual ability to distinguish and locate changes. Acknowledging the inherent correlation between change detection (CD) and RSICC tasks, we believe pixel-level CD is significant for describing the differences between images through language. Regrettably, the current RSICC dataset lacks readily available pixel-level CD labels. To address this deficiency, we leverage a model trained on existing CD datasets to derive CD pseudo-labels. We propose an innovative network with an auxiliary CD branch, supervised by pseudo-labels. Furthermore, a semantic fusion augment (SFA) module is proposed to fuse the feature information extracted by the CD branch, thereby facilitating the nuanced description of changes. Experiments demonstrate that our method achieves state-of-the-art performance and validate that learning pixel-level CD pseudo-labels significantly contributes to change captioning.