Remote sensing (RS) images contain numerous objects of different scales,
which poses significant challenges for the RS image change captioning (RSICC)
task to identify visual changes of interest in complex scenes and describe them
via language. However, current methods still have some weaknesses in
sufficiently extracting and utilizing multi-scale information. In this paper,
we propose a progressive scale-aware network (PSNet) to address the problem.
PSNet is a pure Transformer-based model. To sufficiently extract multi-scale
visual features, multiple progressive difference perception (PDP) layers are
stacked to progressively exploit the differencing features of bitemporal
features. To sufficiently utilize the extracted multi-scale features for
captioning, we propose a scale-aware reinforcement (SR) module and combine it
with the Transformer decoding layer to progressively utilize the features from
different PDP layers. Experiments show that the PDP layer and SR module are
effective and our PSNet outperforms previous methods. Our code is public at
https://github.com/Chen-Yang-Liu/PSNetComment: IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing
Symposiu