Pixel-Level Change Detection Pseudo-Label Learning for Remote Sensing
  Change Captioning
Chenyang Liu Keyan Chen Zipeng Qi Haotian Zhang Zhengxia Zou Zhenwei Shi
The existing methods for Remote Sensing Image Change Captioning (RSICC)
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. Our code will be
available at: https://github.com/Chen-Yang-Liu/Pix4Cap