中国地理科学(英文版) ›› 2009, Vol. 19 ›› Issue (1): 83-88.doi: 10.1007/s11769-009-0083-3

• 论文 • 上一篇    下一篇

Alternative Fuzzy Cluster Segmentation of Remote Sensing Images Based on Adaptive Genetic Algorithm

WANG Jing1,2, TANG Jilong3, LIU Jibin1,4, REN Chunying5, LIU Xiangnan6, FENG Jiang1,2   

  1. 1. Key Laboratory for Wetland Ecology and Vegetation Restoration of National Environmental Protection, Northeast Normal University, Changchun 130024, China;
    2. Key Laboratory of Vegetation Ecology of Education Ministry, Institute of Grassland Science, Northeast Normal University, Changchun 130024, China;
    3. Changchun University of Science and Technology, Changchun 130022, China;
    4. Urban and Town Plan and Design Institute of Jilin Province, Changchun 130061, China;
    5. Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130012, China;
    6. School of Information Engineering, China University of Geosciences, Beijing 100083, China
  • 收稿日期:2008-05-20 修回日期:2008-11-10 出版日期:2009-03-20 发布日期:2010-01-20
  • 通讯作者: FENG Jiang.E-mail:fengj@nenu.edu.cn E-mail:fengj@nenu.edu.cn
  • 基金资助:

    Under the auspices of National Natural Science Foundation of China (No. 30370267);Key Project of Jilin Provincial Science & Technology Department (No. 20075014)

Alternative Fuzzy Cluster Segmentation of Remote Sensing Images Based on Adaptive Genetic Algorithm

WANG Jing1,2, TANG Jilong3, LIU Jibin1,4, REN Chunying5, LIU Xiangnan6, FENG Jiang1,2   

  1. 1. Key Laboratory for Wetland Ecology and Vegetation Restoration of National Environmental Protection, Northeast Normal University, Changchun 130024, China;
    2. Key Laboratory of Vegetation Ecology of Education Ministry, Institute of Grassland Science, Northeast Normal University, Changchun 130024, China;
    3. Changchun University of Science and Technology, Changchun 130022, China;
    4. Urban and Town Plan and Design Institute of Jilin Province, Changchun 130061, China;
    5. Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130012, China;
    6. School of Information Engineering, China University of Geosciences, Beijing 100083, China
  • Received:2008-05-20 Revised:2008-11-10 Online:2009-03-20 Published:2010-01-20
  • Supported by:

    Under the auspices of National Natural Science Foundation of China (No. 30370267);Key Project of Jilin Provincial Science & Technology Department (No. 20075014)

摘要:

Remote sensing image segmentation is the basis of image understanding and analysis. However,the precision and the speed of segmentation can not meet the need of image analysis,due to strong uncertainty and rich texture details of remote sensing images. We proposed a new segmentation method based on Adaptive Genetic Algorithm(AGA) and Alternative Fuzzy C-Means(AFCM). Segmentation thresholds were identified by AGA. Then the image was segmented by AFCM. The results indicate that the precision and the speed of segmentation have been greatly increased,and the accuracy of threshold selection is much higher compared with traditional Otsu and Fuzzy C-Means(FCM) segmentation methods. The segmentation results also show that multi-thresholds segmentation has been achieved by combining AGA with AFCM.

关键词: Adaptive Genetic Algorithm(AGA), Alternative Fuzzy C-Means(AFCM), image segmentation, remote sensing

Abstract:

Remote sensing image segmentation is the basis of image understanding and analysis. However,the precision and the speed of segmentation can not meet the need of image analysis,due to strong uncertainty and rich texture details of remote sensing images. We proposed a new segmentation method based on Adaptive Genetic Algorithm(AGA) and Alternative Fuzzy C-Means(AFCM). Segmentation thresholds were identified by AGA. Then the image was segmented by AFCM. The results indicate that the precision and the speed of segmentation have been greatly increased,and the accuracy of threshold selection is much higher compared with traditional Otsu and Fuzzy C-Means(FCM) segmentation methods. The segmentation results also show that multi-thresholds segmentation has been achieved by combining AGA with AFCM.

Key words: Adaptive Genetic Algorithm(AGA), Alternative Fuzzy C-Means(AFCM), image segmentation, remote sensing