.png){kind=icon}
返回
A Supervised Classification Method Based on Conditional Random Fields With Multiscale Region Connection Calculus Model for SAR Image
.png){kind=icon}
PRE
AI
DOI：10.1109/LGRS.2010.2089427.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This letter presents a supervised classification method for synthetic aperture radar (SAR) images based on multiscale region connection calculus (RCC) and conditional random fields (CRF). Using this method, first, a SAR image is oversegmented into multisuperpixels via the image pyramid. We then use the multiscale RCC model to describe the spatial logic relationships among these superpixels. To complete the process, multiscale RCC relationships are learned and reasoned under the CRF reasoning framework. This method employs iteration strategy for CRF reasoning to get better details in the classification results as well. We illustrate the proposed method by experiments conducted on DLR ESAR image. The results reveal efficient performance.
Keyword:
Conditional random fields (CRF)
ESAR Image
image classification
iteration reasoning
multiscale region connection calculus
期刊
IF:
16.4
论文数:
1.0W
被引数:
5.1K
机构
引用论文
In the Field: Coase an Exemplar in the Tradition of Smith, Marshall and Ostrom在实地研究中：科斯是沿着斯密、马歇尔和奥斯特罗姆传统的典范
OAAI
OAAI分享收藏A new statistical model for Markovian classification of urban areas in high-resolution SAR images高分辨率SAR图像中城市区域马尔可夫分类的新统计模型
PREAI
分享收藏The effect of spin dilution on magnetism of the linear chain system β-Cu2−x Zn x V2O7自旋稀释对线性链体系 β-cu2-x Zn x V2O7磁性的影响.png)
Pramana
PramanaIF0
PREAI
分享收藏
查看更多内容