DocumentCode :
1766706
Title :
An Adaptive Memetic Fuzzy Clustering Algorithm With Spatial Information for Remote Sensing Imagery
Author :
Yanfei Zhong ; Ailong Ma ; Liangpei Zhang
Author_Institution :
State Key Lab. of Inf. Eng. in Surveying, Mapping & Remote Sensing, Wuhan Univ., Wuhan, China
Volume :
7
Issue :
4
fYear :
2014
fDate :
41730
Firstpage :
1235
Lastpage :
1248
Abstract :
Due to its inherent complexity, remote sensing image clustering is a challenging task. Recently, some spatial-based clustering approaches have been proposed; however, one crucial factor with regard to their clustering quality is that there is usually one parameter that controls their spatial information weight, which is difficult to determine. Meanwhile, the traditional optimization methods of the objective functions for these clustering approaches often cannot function well because they cannot simultaneously possess both a local search capability and a global search capability. Furthermore, these methods only use a single optimization method rather than hybridizing and combining the existing algorithmic structures. In this paper, an adaptive fuzzy clustering algorithm with spatial information for remote sensing imagery (AFCM_S1) is proposed, which defines a new objective function with an adaptive spatial information weight by using the concept of entropy. In order to further enhance the capability of the optimization, an adaptive memetic fuzzy clustering algorithm with spatial information for remote sensing imagery (AMASFC) is also proposed. In AMASFC, the clustering problem is transformed into an optimization problem. A memetic algorithm is then utilized to optimize the proposed objective function, combining the global search ability of a differential evolution algorithm with a local search method using Gaussian local search (GLS). The optimal value of the specific parameter in GLS, which determines the local search efficiency, can be obtained by comparing the objective function increment for different values of the parameter. The experimental results using three remote sensing images show that the two proposed algorithms are effective when compared with the traditional clustering algorithms.
Keywords :
entropy; fuzzy systems; optimisation; pattern clustering; remote sensing; Gaussian local search; adaptive memetic fuzzy clustering algorithm; differential evolution algorithm; entropy; global search ability; local search method; optimization; remote sensing imagery; spatial information; Clustering algorithms; Entropy; Linear programming; Memetics; Optimization methods; Remote sensing; Fuzzy clustering; memetic algorithm; remote sensing; spatial information;
fLanguage :
English
Journal_Title :
Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of
Publisher :
ieee
ISSN :
1939-1404
Type :
jour
DOI :
10.1109/JSTARS.2014.2303634
Filename :
6740801
Link To Document :
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