CATNet: Context AggregaTion Network for Instance Segmentation in Remote Sensing Images.
arXiv (Cornell University)(2021)
摘要
The task of instance segmentation in remote sensing images, aiming at
performing per-pixel labeling of objects at instance level, is of great
importance for various civil applications. Despite previous successes, most
existing instance segmentation methods designed for natural images encounter
sharp performance degradations when directly applied to top-view remote sensing
images. Through careful analysis, we observe that the challenges mainly come
from lack of discriminative object features due to severe scale variations, low
contrasts, and clustered distributions. In order to address these problems, a
novel context aggregation network (CATNet) is proposed to improve the feature
extraction process. The proposed model exploits three lightweight plug-and-play
modules, namely dense feature pyramid network (DenseFPN), spatial context
pyramid (SCP), and hierarchical region of interest extractor (HRoIE), to
aggregate global visual context at feature, spatial, and instance domains,
respectively. DenseFPN is a multi-scale feature propagation module that
establishes more flexible information flows by adopting inter-level residual
connections, cross-level dense connections, and feature re-weighting strategy.
Leveraging the attention mechanism, SCP further augments the features by
aggregating global spatial context into local regions. For each instance, HRoIE
adaptively generates RoI features for different downstream tasks. We carry out
extensive evaluation of the proposed scheme on the challenging iSAID, DIOR,
NWPU VHR-10, and HRSID datasets. The evaluation results demonstrate that the
proposed approach outperforms state-of-the-arts with similar computational
costs. Code is available at https://github.com/yeliudev/CATNet.
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关键词
instance segmentation,context aggregation network,remote sensing images,remote sensing
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