Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/75954
Title: A novel adaptive fuzzy local information C-means clustering algorithm for remotely sensed imagery classification
Authors: Zhang, H
Wang, QM
Shi, WZ 
Hao, M
Keywords: Classification
Fuzzy c-means (FCM) clustering
Local measure similarity
Remotely sensed imagery
Spatial information
Issue Date: 2017
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on geoscience and remote sensing, 2017, v. 55, no. 9, p. 5057-5068 How to cite?
Journal: IEEE transactions on geoscience and remote sensing 
Abstract: This paper presents a novel adaptive fuzzy local information c-means (ADFLICM) clustering approach for remotely sensed imagery classification by incorporating the local spatial and gray level information constraints. The ADFLICM approach can enhance the conventional fuzzy c-means algorithm by producing homogeneous segmentation and reducing the edge blurring artifact simultaneously. The major contribution of ADFLICM is use of the new fuzzy local similarity measure based on pixel spatial attraction model, which adaptively determines the weighting factors for neighboring pixel effects without any experimentally set parameters. The weighting factor for each neighborhood is fully adaptive to the image content, and the balance between insensitiveness to noise and reduction of edge blurring artifact to preserve image details is automatically achieved by using the new fuzzy local similarity measure. Four different types of images were used in the experiments to examine the performance of ADFLICM. The experimental results indicate that ADFLICM produces greater accuracy than the other four methods and hence provides an effective clustering algorithm for classification of remotely sensed imagery.
URI: http://hdl.handle.net/10397/75954
ISSN: 0196-2892
EISSN: 1558-0644
DOI: 10.1109/TGRS.2017.2702061
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