Fuzzy Regional MRF Method for Automatic Identification of Urban

CHEN Rong-yuan; XU Xue-song; SHEN Li-zhi; LIU Yue-hua; CHEN Lang

doi:10.3969/j.issn.0372-2112.2019.02.033

您当前的位置：

首页 >

文章列表页 >

Fuzzy Regional MRF Method for Automatic Identification of Urban

更新时间：2025-07-16

- Fuzzy Regional MRF Method for Automatic Identification of Urban
- Acta Electronica Sinica Vol. 47, Issue 2, Pages: 489-494(2019)
- 作者机构：
  
  湖南商学院湖南省移动电子商务协同创新中心, 移动商务智能湖南省重点实验室, 新零售虚拟现实技术湖南省重点实验室,湖南,长沙,410205
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.41101425, No.61471170);Ministry of Education - China Mobile Research Fund (No.MCM20170506);Funded by Science and Technology Program of Education Department of Hunan Province (No.16A114, No.17B145);Key Research and Development Project of Hunan Province (No2018GK2058);Natural Science Foundation of Hunan Province (No.2016JJ2070, No.2017JJ3132)
- DOI：10.3969/j.issn.0372-2112.2019.02.033
  CLC： TP391.41TP751
- Published Online：25 February 2019，
  
  Published：2019
- 稿件说明：
移动端阅览
CHEN Rong-yuan, XU Xue-song, SHEN Li-zhi, et al. Fuzzy Regional MRF Method for Automatic Identification of Urban[J]. Acta Electronica Sinica, 2019, 47(2): 489-494.
DOI：

CHEN Rong-yuan, XU Xue-song, SHEN Li-zhi, et al. Fuzzy Regional MRF Method for Automatic Identification of Urban[J]. Acta Electronica Sinica, 2019, 47(2): 489-494. DOI： 10.3969/j.issn.0372-2112.2019.02.033.

摘要

针对遥感影像中城镇区域内外的自然地物难以区分，城镇区域不易完整识别的问题，提出一种对象级模糊MRF识别方法.该方法首先通过光谱信息和空间梯度分析得到城镇种子点（人造地物顶部点和阴影点）；然后由均值漂移算法过分割影像；再对过分割区域建立MRF，在迭代过程中用MRF的条件概率矩阵代替模糊C均值聚类算法的隶属度矩阵，并保持包含种子点的区域类别不变，从而实现城镇识别.对于QuickBird和Ikonos遥感影像，该模型能够兼顾城镇区域自动识别过程中的随机性与模糊性，很好地利用了空间相关信息，有效识别出了城镇区域.

Abstract

In order to effectively distinguish the natural objects inside the town from outside objects

and completely identify the urban regions in remote sensing image

a fuzzy geographic object-based MRF method is proposed.Firstly

the seed points of the town

i.e.

the top and shadow points of artificial ground objects

are firstly obtained by analyzing spectral information and spatial gradient.Then the over-segmented regions of the origin image are obtained by Mean Shift algorithm.Finally

a MRF is established over regions.and the membership matrix in the fuzzy C-means clustering algorithm is replaced by the conditional probability matrix in the MRF in an iterative manner.Meanwhile the categories of the regions containing the seed points are kept unchanged.For QuickBird and Ikonos remote sensing images

the proposed model can simultaneously deal with both the stochastic and fuzzy nature of images

and effectively intergate the space information

and thus benefit the identification of town.

关键词

Keywords

references

Views

262

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Enhanced PFCM Algorithm for Noisy Image Segmentation Combining Regional and Neighborhood-Level Information

A Fuzzy Weighted Sum Validity Function for Clustering with a Mixed Strategy Evolutionary Algorithm

Spectral Reweighted Collaborative Sparsity and Total Variation Based Hyperspectral Unmixing Method

Research of Moving Object Detection Based on Deep Frame Difference Convolution Neural Network

Related Author

CHEN Hao-ran

WANG Hai-zhou

WANG Xiao-peng

SHANG Wen-qian

HE Jun

ZHOU Cheng

HUANG Hou-kuan

DONG Hong-bin

Related Institution

School of Electronic and Information Engineering, Lanzhou Jiaotong University

School of Computer and Information Technology,Beijing Jiaotong University.

Department of Computer Science ,Harbin Normal University

School of Computer and Information TechnologyBeijing Jiaotong University.Beijing 100044China

Department of Computer Science Harbin Normal UniversityHarbinHeilongjiang 150080China

⁰