Alternative Fuzzy Cluster segmentation of remote sensing images based on Adaptive Genetic Algorithm |
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| Authors: | Jing Wang Jilong Tang Jibin Liu Chunying Ren Xiangnan Liu Jiang Feng |
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| Affiliation: | [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, Changehun 130024, China [3]Changehun University of Seienee and Technology, Changchun 130022, China [4]Urban and Town Plan and Design Institute of Jilin Province, Changehun 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 |
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| 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. |
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| Keywords: | Adaptive Genetic Algorithm (AGA) Alternative Fuzzy C-Means (AFCM) image segmentation remote sensing |
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