A Novel Adaptive Fuzzy Local Information C -Means Clustering Algorithm for Remotely Sensed Imagery Classification

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 produc...

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Published in:	IEEE transactions on geoscience and remote sensing Vol. 55; no. 9; pp. 5057 - 5068
Main Authors:	Zhang, Hua, Wang, Qunming, Shi, Wenzhong, Hao, Ming
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.09.2017 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithms Blurring Classification Clustering Clustering algorithms fuzzy c-means (FCM) clustering Image classification Image edge detection Image processing Image segmentation Imagery Linear programming local measure similarity Nickel Noise measurement Noise reduction Pixels Remote sensing remotely sensed imagery Robustness Similarity Similarity measures spatial information Weighting
ISSN:	0196-2892, 1558-0644
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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.
AbstractList	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.
Author	Qunming Wang Ming Hao Hua Zhang Wenzhong Shi
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SubjectTerms	Algorithms Blurring Classification Clustering Clustering algorithms fuzzy c-means (FCM) clustering Image classification Image edge detection Image processing Image segmentation Imagery Linear programming local measure similarity Nickel Noise measurement Noise reduction Pixels Remote sensing remotely sensed imagery Robustness Similarity Similarity measures spatial information Weighting
Title	A Novel Adaptive Fuzzy Local Information C -Means Clustering Algorithm for Remotely Sensed Imagery Classification
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