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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Veröffentlicht in:	IEEE transactions on geoscience and remote sensing Jg. 55; H. 9; S. 5057 - 5068
Hauptverfasser:	Zhang, Hua, Wang, Qunming, Shi, Wenzhong, Hao, Ming
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.09.2017 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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
Online-Zugang:	Volltext
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