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ResiDualGAN: Resize-Residual DualGAN for Cross-Domain Remote Sensing Images Semantic Segmentation
The performance of a semantic segmentation model for remote sensing (RS)
images pretrained on an annotated dataset would greatly decrease when testing
on another unannotated dataset because of the domain gap. Adversarial
generative methods, e.g., DualGAN, are utilized for unpaired image-to-image
translation to minimize the pixel-level domain gap, which is one of the common
approaches for unsupervised domain adaptation (UDA). However, the existing
image translation methods are facing two problems when performing RS images
translation: 1) ignoring the scale discrepancy between two RS datasets which
greatly affects the accuracy performance of scale-invariant objects, 2)
ignoring the characteristic of real-to-real translation of RS images which
brings an unstable factor for the training of the models. In this paper,
ResiDualGAN is proposed for RS images translation, where an in-network resizer
module is used for addressing the scale discrepancy of RS datasets, and a
residual connection is used for strengthening the stability of real-to-real
images translation and improving the performance in cross-domain semantic
segmentation tasks. Combined with an output space adaptation method, the
proposed method greatly improves the accuracy performance on common benchmarks,
which demonstrates the superiority and reliability of ResiDuanGAN. At the end
of the paper, a thorough discussion is also conducted to give a reasonable
explanation for the improvement of ResiDualGAN. Our source code is available at
https://github.com/miemieyanga/ResiDualGAN-DRDG
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