Satellite Image Segmentation Using Self-Organizing Maps and Fuzzy C-Means

被引:10
作者
Awad, Mohamad M. [1 ]
Nasri, Ahmad [2 ]
机构
[1] Natl Council Sci Res, POB 11-8281, Beirut 11072260, Lebanon
[2] Amer Univ Beirut, Dept Comp Sci, Beirut, Lebanon
来源
2009 IEEE INTERNATIONAL SYMPOSIUM ON SIGNAL PROCESSING AND INFORMATION TECHNOLOGY (ISSPIT 2009) | 2009年
关键词
Segmentation; Remote sensing; Self-Organizing Maps; Fuzzy C-Means; Unsupervised; Satellite image; GENETIC ALGORITHM; CLASSIFICATION;
D O I
10.1109/ISSPIT.2009.5407521
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
The quality of image interpretation depends strongly on the segmentation process which is an important step in image processing. Most of the segmentation methods and approaches are not suitable for noisy environments such as satellite images of high resolution. Sometime they require a priori knowledge, and another time they do not work on all types of images. Self-Organizing Maps (SOMs) and Fuzzy C-Means (FCM) segmentation methods are widely used to process different types of simple and complex images. These two important known methods are reviewed, and summarized. In addition, a new approach is created based on SOMs and FCM. The reason for combining both methods is to create an unsupervised parameter free approach. The new approach is applied on two different types of medium and high resolution satellite images in order to examine the accuracy of the segmentation methods and the new approach. This paper and the results of experiments provide the reader with information about the improvement obtained by this approach compared to known commercial segmentation method.
引用
收藏
页码:398 / +
页数:2
相关论文
共 15 条
[1]  
[Anonymous], Pattern Recognition with Fuzzy Objective Function Algorithms
[2]   Multi-component image segmentation using a hybrid dynamic genetic algorithm and fuzzy C-means [J].
Awad, M. ;
Chehdi, K. ;
Nasri, A. .
IET IMAGE PROCESSING, 2009, 3 (02) :52-62
[3]   Multicomponent image segmentation using a genetic algorithm and artificial neural network [J].
Awad, Mohamad ;
Chehdi, Kacem ;
Nasri, Ahmad .
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2007, 4 (04) :571-575
[4]   Automatic segmentation and classification of outdoor images using neural networks [J].
Campbell, NW ;
Thomas, BT ;
Troscianko, T .
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS, 1997, 8 (01) :137-144
[5]   Robust image segmentation using genetic algorithm with a fuzzy measure [J].
Chun, DN ;
Yang, HS .
PATTERN RECOGNITION, 1996, 29 (07) :1195-1211
[6]  
Fauzi M.F., 2003, P BRIT MACHINE C, P519
[7]  
KOHAVI R, 1998, SPECIAL ISSUE APPL M, V30
[8]  
Kohonen T., 2001, SPRINGER SERIES INFO, V30, P1, DOI 10.1007/978-3-642-56927-2
[9]   Neural techniques for image segmentation [J].
Marsella, M ;
Miranda, S .
IEEE INTERNATIONAL JOINT SYMPOSIA ON INTELLIGENCE AND SYSTEMS - PROCEEDINGS, 1998, :367-372
[10]  
Noordam J., 2000, Proc. Of 15th International Conference on Pattern Recognition (ICPR'00) 1, P1462