  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 they are directly applied to top-view
remote sensing images. Through careful analysis, we observe that the challenges
mainly come from the 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.
Extensive evaluations of the proposed scheme on iSAID, DIOR, NWPU VHR-10, and
HRSID datasets demonstrate that the proposed approach outperforms
state-of-the-arts under similar computational costs. Source code and
pre-trained models are available at https://github.com/yeliudev/CATNet.
