  Remote sensing change detection between bi-temporal images receives growing
concentration from researchers. However, comparing two bi-temporal images for
detecting changes is challenging, as they demonstrate different appearances. In
this paper, we propose a dual attentive generative adversarial network for
achieving very high-resolution remote sensing image change detection tasks,
which regards the detection model as a generator and attains the optimal
weights of the detection model without increasing the parameters of the
detection model through generative-adversarial strategy, boosting the spatial
contiguity of predictions. Moreover, We design a multi-level feature extractor
for effectively fusing multi-level features, which adopts the pre-trained model
to extract multi-level features from bi-temporal images and introduces
aggregate connections to fuse them. To strengthen the identification of
multi-scale objects, we propose a multi-scale adaptive fusion module to
adaptively fuse multi-scale features through various receptive fields and
design a context refinement module to explore contextual dependencies.
Moreover, the DAGAN framework utilizes the 4-layer convolution network as a
discriminator to identify whether the synthetic image is fake or real.
Extensive experiments represent that the DAGAN framework has better performance
with 85.01% mean IoU and 91.48% mean F1 score than advanced methods on the
LEVIR dataset.

Computer Vision and Pattern Recognition;Image and Video Processing