期刊
REMOTE SENSING
卷 14, 期 6, 页码 -出版社
MDPI
DOI: 10.3390/rs14061502
关键词
polarized SAR image semantic segmentation; conditional random field; sensitivity analysis; multi-size input
类别
资金
- National Natural Science Foundation of China [61501228]
- Natural Science Foundation of Jiangsu [BK20140825]
- Aeronautical Science Foundation of China [20152052029, 20182052012]
- Basic Research [NS2015040]
- National Science
- and Technology Major Project [2017-II-0001-0017]
This paper proposes a polarization SAR image semantic segmentation method based on a dual-channel multi-size fully connected convolutional conditional random field. By inputting the full-polarization SAR image and the corresponding optical image simultaneously, integrating multi-size inputs, and introducing the importance of features, the accuracy of image segmentation is improved.
The traditional fully connected convolutional conditional random field has a proven robust performance in post-processing semantic segmentation of SAR images. However, the current challenge is how to improve the richness of image features, thereby improving the accuracy of image segmentation. This paper proposes a polarization SAR image semantic segmentation method based on a dual-channel multi-size fully connected convolutional conditional random field. Firstly, the full-polarization SAR image and the corresponding optical image are input into the model at the same time, which can increase the richness of feature information. Secondly, multi-size input integrates image information of different sizes and models images of various sizes. Finally, the importance of features is introduced to determine the weights of polarized SAR images and optical images, and CRF is improved into a potential function so that the model can adaptively adjust the degree of influence of different image features on the segmentation effect. The experimental results show that the proposed method achieves the highest mean intersection over union (mIoU) and global accuracy (GA) with the least running time, which verifies the effectiveness of our method.
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