论文标题
高空间分辨率遥感图像中的地理空间对象细分的前景感知关系网络
Foreground-Aware Relation Network for Geospatial Object Segmentation in High Spatial Resolution Remote Sensing Imagery
论文作者
论文摘要
地理空间对象分割是一种特定的语义分割任务，总是面对更大的变化，较大的背景内阶层差异以及高空间分辨率（HSR）遥感感测图像中的前景不平衡。但是，一般的语义分割方法主要集中于自然场景中的比例变化，而对其他两个问题通常发生在大面积地球观测场景中的考虑不足。在本文中，我们认为问题在于缺乏前景建模，并从基于关系和基于优化的前景建模的角度提出了一个前景感知的关系网络（FARSEG），以减轻上述两个问题。从关系的角度来看，Farseg通过学习前景与现场关系相关的前景环境来增强对前景特征的歧视。同时，从优化的角度来看，提出了一种前景感知的优化，以关注培训期间的前景示例和背景的艰难示例，以进行平衡优化。使用大型数据集获得的实验结果表明，所提出的方法优于最新的一般语义分割方法，并且在速度和准确性之间取得了更好的权衡。代码已在：\ url {https://github.com/z-zheng/farseg}中提供。
Geospatial object segmentation, as a particular semantic segmentation task, always faces with larger-scale variation, larger intra-class variance of background, and foreground-background imbalance in the high spatial resolution (HSR) remote sensing imagery. However, general semantic segmentation methods mainly focus on scale variation in the natural scene, with inadequate consideration of the other two problems that usually happen in the large area earth observation scene. In this paper, we argue that the problems lie on the lack of foreground modeling and propose a foreground-aware relation network (FarSeg) from the perspectives of relation-based and optimization-based foreground modeling, to alleviate the above two problems. From perspective of relation, FarSeg enhances the discrimination of foreground features via foreground-correlated contexts associated by learning foreground-scene relation. Meanwhile, from perspective of optimization, a foreground-aware optimization is proposed to focus on foreground examples and hard examples of background during training for a balanced optimization. The experimental results obtained using a large scale dataset suggest that the proposed method is superior to the state-of-the-art general semantic segmentation methods and achieves a better trade-off between speed and accuracy. Code has been made available at: \url{https://github.com/Z-Zheng/FarSeg}.
