Abstract
When segmenting massive amounts of remote sensing images collected from different satellites or geographic locations (cities), the pre-trained deep learning models cannot always output satisfactory predictions. To deal with this issue, domain adaptation has been widely utilized to enhance the generalization abilities of the segmentation models. Most of the existing domain adaptation methods, which based on image-to-image translation, firstly transfer the source images to the pseudo-target images, adapt the classifier from the source domain to the target domain. However, these unidirectional methods suffer from the following two limitations: (1) they do not consider the inverse procedure and they cannot fully take advantage of the information from the other domain, which is also beneficial, as confirmed by our experiments; (2) these methods may fail in the cases where transferring the source images to the pseudo-target images is difficult. In this paper, in order to solve these problems, we propose a novel framework BiFDANet for unsupervised bidirectional domain adaptation in the semantic segmentation of remote sensing images. It optimizes the segmentation models in two opposite directions. In the source-to-target direction, BiFDANet learns to transfer the source images to the pseudo-target images and adapts the classifier to the target domain. In the opposite direction, BiFDANet transfers the target images to the pseudo-source images and optimizes the source classifier. At test stage, we make the best of the source classifier and the target classifier, which complement each other with a simple linear combination method, further improving the performance of our BiFDANet. Furthermore, we propose a new bidirectional semantic consistency loss for our BiFDANet to maintain the semantic consistency during the bidirectional image-to-image translation process. The experiments on two datasets including satellite images and aerial images demonstrate the superiority of our method against existing unidirectional methods.
Highlights
In the last few years, it has been possible to collect a mass of remote sensing images, thanks to the continuous advancement of remote sensing techniques
For BiFDANet, we report the results obtained by the source classifier FS and the target classifier FT separately before the linear combination method and obtained by taking the intersection or union of the predicted results of the two classifiers FS and FT
We can observe that all components help our framework to achieve better IoU and F1 scores, and the proposed bidirectional semantic consistency loss could further improve the performance of the models, which demonstrates the effectiveness of our bidirectional semantic consistency loss again
Summary
In the last few years, it has been possible to collect a mass of remote sensing images, thanks to the continuous advancement of remote sensing techniques. For the semantic segmentation of remote sensing images, CNN [4] has become one of the most efficient methods in the past decades and several CNN models have shown their effectiveness, such as DeepLab [5] and its variants [6,7] These methods have some limitations, because CNN-based architectures tend to be sensitive to the distributions and features of the training images and test images. Domain gap problems are often caused due to many reasons, such as illumination conditions, imaging times, imaging sensors, geographic locations and so on These factors will change the spectral characteristics of objects and resulted in a large intra-class variability. A few images may consist of near-infrared, green, and red channels while the others may have green, red, and blue bands
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