多任务协同的多模态遥感目标分割算法

Multitask Collaborative Multi-modal Remote Sensing Target Segmentation Algorithm

利用语义分割技术提取的高分辨率遥感影像目标分割具有重要的应用前景。随着多传感器技术的飞速发展,多模态遥感影像间良好的优势互补性受到广泛关注,对其联合分析成为研究热点。该文同时分析光学遥感影像和高程数据,并针对现实场景中完全配准的高程数据不足导致两类数据融合分类精度不足的问题,提出一种基于多模态遥感数据的多任务协同模型(UR-PSPNet),该模型提取光学图像的深层特征,预测语义标签和高程值,并将高程数据作为监督信息嵌入,以提升目标分割的准确性。该文设计了基于ISPRS的对比实验,证明了该算法可以更好地融合多模态数据特征,提升了光学遥感影像目标分割的精度。

The use of semantic segmentation technology to extract high-resolution remote sensing image object segmentation has important application prospects.With the rapid development of multi-sensor technology,the good complementary advantages between multimodal remote sensing images have received widespread attention,and joint analysis of them has become a research hotspot.This article analyzes both optical remote sensing images and elevation data,and proposes a multi-task collaborative model based on multimodal remote sensing data(United Refined PSPNet,UR-PSPNet)to address the issue of insufficient fusion classification accuracy of the two types of data due to insufficient fully registered elevation data in real scenarios.This model extracts deep features of optical images,predicts semantic labels and elevation values,and embeds elevation data as supervised information,to improve the accuracy of target segmentation.This article designs a comparative experiment based on ISPRS,which proves that this algorithm can better fuse multimodal data features and improve the accuracy of object segmentation in optical remote sensing images.