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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 visualability to distinguish and locate changes. Acknowledging the inherentcorrelation between change detection (CD) and RSICC tasks, we believepixel-level CD is significant for describing the differences between imagesthrough language. Regrettably, the current RSICC dataset lacks readilyavailable pixel-level CD labels. To address this deficiency, we leverage amodel trained on existing CD datasets to derive CD pseudo-labels. We propose aninnovative network with an auxiliary CD branch, supervised by pseudo-labels.Furthermore, a semantic fusion augment (SFA) module is proposed to fuse thefeature information extracted by the CD branch, thereby facilitating thenuanced description of changes. Experiments demonstrate that our methodachieves state-of-the-art performance and validate that learning pixel-level CDpseudo-labels significantly contributes to change captioning. Our code will beavailable at: https://github.com/Chen-Yang-Liu/Pix4Cap

Pixel-Level Change Detection Pseudo-Label Learning for Remote Sensing Change Captioning