Enhanced Spatially Constrained Remotely Sensed Imagery Classification Using a Fuzzy Local Double Neighborhood Information C-Means Clustering Algorithm

This paper presents a fuzzy local double neighborhood information c-means (FLDNICM) clustering algorithm for remotely sensed imagery classification, which incorporates flexible and accurate local spatial and spectral information. First, a tradeoff weighted fuzzy factor is established based on a pixe...

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Vydané v:	IEEE journal of selected topics in applied earth observations and remote sensing Ročník 11; číslo 8; s. 2896 - 2910
Hlavní autori:	Zhang, Hua, Bruzzone, Lorenzo, Shi, Wenzhong, Hao, Ming, Wang, Yunjia
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 01.08.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Blurring Classification Clustering Clustering algorithms Clustering methods Conditional probability Dependence fuzzy c-means (FCM) clustering Fuzzy logic Image classification Image edge detection Image enhancement Image processing Image segmentation Imagery Linear programming neighborhood Noise measurement Pixels prior probability Probability theory Remote sensing remotely sensed imagery Robustness Robustness (mathematics) Tradeoffs
ISSN:	1939-1404, 2151-1535
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