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.

翻译：机载智能处理广泛应用于遥感领域的应急任务中。然而，该方法主要局限于单一平台，存在观测范围有限且易受干扰的问题，导致精度受限。基于多平台协同观测的现状，本文创新性地提出了一种名为DCP-Net的分布式协同感知网络。首先，所提出的DCP-Net通过融合其他平台的特征来提升各成员的感知性能。其次，设计了一个自互信息匹配模块，用于识别协同机会并选择合适伙伴，优先处理关键协同特征并降低冗余传输成本。第三，构建了关联特征融合模块，以解决局部特征与协同特征之间的错位问题，提升下游任务中融合特征的质量。我们利用Potsdam、iSAID和DFC23三个语义分割数据集进行了广泛实验与可视化分析。结果表明，DCP-Net全面优于现有方法，在最高协同效率下将mIoU提升了2.61%~16.89%，使性能达到先进水平。