地理空间信息2026，Vol.24Issue(3)：78-83,6.DOI:10.3969/j.issn.1672-4623.2026.03.016
融合双路径编码的无人机影像灾后损毁地物识别
Post-disaster Damaged Terrain Object Recognition in UAV Images Using Dual-path Coding Fusion
摘要
Abstract
With the rapid development of UAV remote sensing technology,high-resolution image has been widely utilized for post-disaster damaged terrain object recognition.However,the traditional methods often struggle to capture rich spatial textures and semantic information effectively,which leads to blurred boundaries,loss of fine details,and omission of small-scale targets.To overcome these issues,we proposed a dual-path coding fusion approach for damaged terrain object recognition in UAV images.The proposed method incorporates a dual-path coding architecture based on convolutional neural network and Transformer,which could extract both local texture features and global contextual representations,addressing the shortcomings of single network architecture.Additionally,a refined multi-scale pyramid pooling module is introduced to integrate features at different scales,enhancing the model's ability to recognize terrain objects with varying shapes and sizes.Furthermore,a dynamic semantic prompting mechanism based on category prior knowledge is proposed to improve discrimination in low-contrast and complex scenes.Experimental results demonstrate that the proposed method outperforms mainstream models in terms of overall accuracy and other key evaluation metrics,significantly improving the precision of post-disaster damaged terrain object recognition.关键词
无人机影像/语义分割/SAM/损毁地物识别Key words
UAV image/semantic segmentation/SAM/damaged terrain object recognition分类
天文与地球科学引用本文复制引用
邱文剑,王晓楠,赵静,曾文浩,曾飞雪..融合双路径编码的无人机影像灾后损毁地物识别[J].地理空间信息,2026,24(3):78-83,6.基金项目
基于遥感影像AI智能判别的地质灾害应急测绘响应技术研究(ZRZY2025KJ50). （ZRZY2025KJ50）
