地球信息科学学报 ›› 2023, Vol. 25 ›› Issue (10): 2012-2025.doi: 10.12082/dqxxkx.2023.230171
蒋伟杰(), 张春菊*(
), 徐兵, 罗晨晨, 周晗, 周康
收稿日期:
2023-04-03
修回日期:
2023-06-01
出版日期:
2023-10-25
发布日期:
2023-09-22
通讯作者:
* 张春菊(1984—),女,安徽宿州人,博士,副教授,主要从事地理信息智能处理与服务研究。 E-mail: zcjtwz@sina.com作者简介:
蒋伟杰(1998—),男,安徽阜阳人,硕士生,主要从事遥感影像智能处理与研究。E-mail: jwj_1219@163.com
基金资助:
JIANG Weijie(), ZAHNG Chunju*(
), XU Bing, LUO Chenchen, ZHOU Han, ZHOU Kang
Received:
2023-04-03
Revised:
2023-06-01
Online:
2023-10-25
Published:
2023-09-22
Contact:
* ZHANG Chunju, E-mail: Supported by:
摘要:
遥感影像蕴含丰富的语义信息,在滑坡灾害监测任务中发挥出了重要的作用。传统的滑坡识别主要通过遥感目视解译和人机交互识别,存在耗时费力、主观性强和提取精度低等问题。语义分割作为深度学习中的一项重要任务,因其端到端的像素级分类能力,已在遥感影像自动化识别任务中发挥出了重要作用。现有遥感影像滑坡灾害语义分割模型通常无法顾及多尺度地物特征,且随着网络深度增加会造成边界模糊等问题。本文提出了AED-Net(Attention combined with Encoder-Decoder Network),使用浅层特征提取网络缓解深度神经网络造成的边界模糊问题,利用空洞空间卷积池化金字塔结构的多尺度特征提取能力,结合编码器-解码器结构的特征还原能力还原边界信息,并使用通道注意力机制强化模型的关键特征学习能力。利用GID-5数据集针对模型中空洞卷积的膨胀率设置、通道注意力机制的选择进行对比试验以得到最优解,最终得到的模型在毕节市滑坡灾害数据集上获得了最优表现,像素准确度为95.58%,平均像素精度为89.24%,平均交互比为82.68%,相比PSP-Net、Attention U-Net、加入ECA注意力机制的DeeplabV3+、PA-Fov、LandsNet等语义分割模型,PA提升了0.73%~1.97%,MPA提升了1.0%~2.84%,MIoU提升了2.25%~5.11%,达到了最优分割效果。
蒋伟杰, 张春菊, 徐兵, 罗晨晨, 周晗, 周康. AED-Net:滑坡灾害遥感影像语义分割模型[J]. 地球信息科学学报, 2023, 25(10): 2012-2025.DOI:10.12082/dqxxkx.2023.230171
JIANG Weijie, ZAHNG Chunju, XU Bing, LUO Chenchen, ZHOU Han, ZHOU Kang. AED-Net: Semantic Segmentation Model for Landslide Recognition from Remote Sensing Images[J]. Journal of Geo-information Science, 2023, 25(10): 2012-2025.DOI:10.12082/dqxxkx.2023.230171
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