期刊
REMOTE SENSING
卷 13, 期 18, 页码 -出版社
MDPI
DOI: 10.3390/rs13183585
关键词
semantic segmentation; remote sensing; deep learning; pure Efficient transformer; Swin transformer; edge
类别
资金
- Fundamental Research Funds for the China Central Universities of USTB [FRF-DF-19-002]
- Scientific and Technological Innovation Foundation of Shunde Graduate School, USTB [BK20BE014]
This study introduces a novel Efficient Transformer model to address edge classification issues in remote sensing image semantic segmentation, significantly improving accuracy while balancing computational complexity. By accelerating inference speed and introducing edge enhancement methods, improvements were achieved on the Potsdam and Vaihingen datasets.
Semantic segmentation for remote sensing images (RSIs) is widely applied in geological surveys, urban resources management, and disaster monitoring. Recent solutions on remote sensing segmentation tasks are generally addressed by CNN-based models and transformer-based models. In particular, transformer-based architecture generally struggles with two main problems: a high computation load and inaccurate edge classification. Therefore, to overcome these problems, we propose a novel transformer model to realize lightweight edge classification. First, based on a Swin transformer backbone, a pure Efficient transformer with mlphead is proposed to accelerate the inference speed. Moreover, explicit and implicit edge enhancement methods are proposed to cope with object edge problems. The experimental results evaluated on the Potsdam and Vaihingen datasets present that the proposed approach significantly improved the final accuracy, achieving a trade-off between computational complexity (Flops) and accuracy (Efficient-L obtaining 3.23% mIoU improvement on Vaihingen and 2.46% mIoU improvement on Potsdam compared with HRCNet_W48). As a result, it is believed that the proposed Efficient transformer will have an advantage in dealing with remote sensing image segmentation problems.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据