A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf
.
Cycle and Self-Supervised Consistency Training for Adapting Semantic Segmentation of Aerial Images
2022
Remote Sensing
Semantic segmentation is a critical problem for many remote sensing (RS) image applications. Benefiting from large-scale pixel-level labeled data and the continuous evolution of deep neural network architectures, the performance of semantic segmentation approaches has been constantly improved. However, deploying a well-trained model on unseen and diverse testing environments remains a major challenge: a large gap between data distributions in train and test domains results in severe performance
doi:10.3390/rs14071527
doaj:3e371156360c450ea7b23f242d99e34f
fatcat:offdwang4va2hbolpbcwu2mp44