| 研究実績の概要 |
The research achievements can be summarized as follows:
1. We have proposed novel deep neural network architectures, including image adaptation, feature adaptation, knowledge distillation, and self-training (SL) modules to solve the differ-modality learning problem.
2. We have proposed a new coarse-to-fine framework for detecting floods at a large scale. Multiple source satellite datasets, including the Gaofen (GF) series and Zhuhai-1 hyperspectral, were provided to detect and monitor the floods.
3. We have proposed self-paced positive-unlabeled learning (PU) to build damage assessment with very limited labeled data and many unlabeled data.
4. We have proposed an object detection method and a background-enhancement method for building damage mapping. The experiment using both background-enhanced samples and landslide-inducing information showed a great performance.
|
