Global river monitoring is an important mission within the remote sensing society. One of the main challenges faced by this mission is generating an accurate water mask from remote sensing images (RSI) of rivers (RSIR), especially on a global scale with various river features. Aiming at better water area classification using semantic information, this paper presents a segmentation method for global river monitoring based on semantic clustering and semantic fusion. Firstly, an encoder-decoder network (AEN)-based architecture is proposed to obtain the semantic features from RSIR. Secondly, a clustering-based semantic fusion method is proposed to divide semantic features of RSIR into groups and train convolutional neural networks (CNN) models corresponding to each group using data augmentation and semi-supervised learning. Thirdly, a semantic distance-based segmentation fusion method is proposed for fusing the CNN models result into final segmentation mask. We built a global river dataset that contains multiple river segments from each continent of the world based on Sentinel-2 satellite imagery. The result shows that the F1-score of the proposed segmentation method is 93.32%, which outperforms several state-of-the-art algorithms, and demonstrates that grouping semantic information helps better segment the RSIR in global scale.
Bibliographical noteFunding Information:
Funding: This research was funded by the Project for the National Natural Science Foundation of China, grant number 61672064, and Beijing Laboratory of Advanced Information Networks, grant number 0040000546319003 and PXM2019_014204_500029, and Basic Research Program of Qinghai Province, grant number 2020-ZJ-709.
- Encoder-decoder network
- Feature extraction
- Remote sensing image of river
- Semantic fusion
- Semi-supervised learning