4.8 Article

FarSeg plus plus : Foreground-Aware Relation Network for Geospatial Object Segmentation in High Spatial Resolution Remote Sensing Imagery

出版社

IEEE COMPUTER SOC
DOI: 10.1109/TPAMI.2023.3296757

关键词

Remote sensing; Feature extraction; Object segmentation; Decoding; Semantic segmentation; Optimization; Semantics; Foreground modeling; object segmentation; semantic segmentation; remote sensing; deep learning

向作者/读者索取更多资源

In this paper, a foreground-aware relation network (FarSeg++) is proposed to address the issues of scale variation, large intra-class variance of background, and foreground-background imbalance in high spatial resolution remote sensing imagery. The network improves the discrimination of foreground features, achieves balanced optimization, and enhances objectness representation. Experimental results demonstrate that FarSeg++ outperforms state-of-the-art semantic segmentation methods and achieves a better trade-off between speed and accuracy.
Geospatial object segmentation, a fundamental Earth vision task, always suffers from scale variation, the larger intra-class variance of background, and foreground-background imbalance in high spatial resolution (HSR) remote sensing imagery. Generic semantic segmentation methods mainly focus on the scale variation in natural scenarios. However, the other two problems are insufficiently considered in large area Earth observation scenarios. In this paper, we propose a foreground-aware relation network (FarSeg++) from the perspectives of relation-based, optimization-based, and objectness-based foreground modeling, alleviating the above two problems. From the perspective of the relations, the foreground-scene relation module improves the discrimination of the foreground features via the foreground-correlated contexts associated with the object-scene relation. From the perspective of optimization, foreground-aware optimization is proposed to focus on foreground examples and hard examples of the background during training to achieve a balanced optimization. Besides, from the perspective of objectness, a foreground-aware decoder is proposed to improve the objectness representation, alleviating the objectness prediction problem that is the main bottleneck revealed by an empirical upper bound analysis. We also introduce a new large-scale high-resolution urban vehicle segmentation dataset to verify the effectiveness of the proposed method and push the development of objectness prediction further forward. The experimental results suggest that FarSeg++ is superior to the state-of-the-art generic semantic segmentation methods and can achieve a better trade-off between speed and accuracy.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.8
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据