FactSeg: Foreground Activation Driven Small Object Semantic Segmentation in Large-Scale Remote Sensing Imagery TGRS 2021
- Ailong Ma
- Junjue Wang
- Yanfei Zhong
- Zhuo Zheng
Abstract
This is follow-up work of our FarSeg (CVPR2020)
The small object semantic segmentation task is aimed at automatically extracting key objects from high resolution remote sensing (HRS) imagery. Compared with the large-scalecoverage areas for remote sensing imagery, the key objects such as cars, ships, etc. in HRS imagery often contain only a few pixels. In this paper, to tackle this problem, the foreground activation (FA) driven small object semantic segmentation (FactSeg) framework is proposed from perspectives of structure and optimization. In the structure design, FA object representation is proposed to enhance the awareness of the weak features in small objects. The FA object representation framework is made up of a dual-branch decoder and collaborative probability (CP) loss. In the dual-branch decoder, the FA branch is designed to activate the small object features (activation), as well as suppress the largescale background, and the semantic refinement (SR) branch is designed to further distinguish small objects (refinement). The CP loss is proposed to effectively combine the activation and refinement outputs of the decoder under the CP hypothesis. During the collaboration, the weak features of the small objects are enhanced with the activation output, and the refined output can be viewed as the refinement of the binary outputs. In the optimization stage, small object mining (SOM) based network optimization is applied to automatically select effective samples, to refine the direction of the optimization, while addressing the imbalanced sample problem between the small objects and the large-scale background. The experimental results obtained with two benchmark HRS imagery segmentation datasets demonstrate that the proposed framework outperforms the state-of-the-art semantic segmentation methods, and achieves a good tradeoff between accuracy and efficiency.
Experiments on iSAID
BibTeX
author={Ma Ailong, Wang Junjue, Zhong Yanfei and Zheng Zhuo},
journal={IEEE Transactions on Geoscience and Remote Sensing},
title={FactSeg: Foreground Activation Driven Small Object Semantic Segmentation in Large-Scale Remote Sensing Imagery},
year={2021},
volume={},
number={},
pages={1-16},
doi={10.1109/TGRS.2021.3097148}
}
@inproceedings{zheng2020foreground,
title={Foreground-Aware Relation Network for Geospatial Object Segmentation in High Spatial Resolution Remote Sensing Imagery},
author={Zheng, Zhuo and Zhong, Yanfei and Wang, Junjue and Ma, Ailong},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={4096--4105},
year={2020}
}
