DCP-Net: A Distributed Collaborative Perception Network for Remote
Sensing Semantic Segmentation
Abstract
Onboard intelligent processing is widely applied in emergency tasks in
the field of remote sensing. However, it is predominantly confined to an
individual platform with a limited observation range as well as
susceptibility to interference, resulting in limited accuracy.
Considering the current state of multi-platform collaborative
observation, this article innovatively presents a distributed
collaborative perception network called DCP-Net. Firstly, the proposed
DCP-Net helps members to enhance perception performance by integrating
features from other platforms. Secondly, a self-mutual information match
module is proposed to identify collaboration opportunities and select
suitable partners, prioritizing critical collaborative features and
reducing redundant transmission cost.
Thirdly, a related feature fusion module is designed to address the
misalignment between local and collaborative features, improving the
quality of fused features for the downstream task.
We conduct extensive experiments and visualization analyses using three
semantic segmentation datasets, including Potsdam, iSAID and DFC23. The
results demonstrate that DCP-Net outperforms the existing methods
comprehensively, improving mIoU by 2.61%~16.89% at the
highest collaboration efficiency, which promotes the performance to a
state-of-the-art level.