An Unsupervised Artificial Neural Network Method for Satellite Image Segmentation

Image segmentation is an essential step in image processing. The goal of segmentation is to simplify and/or to change the representation of an image into a form easier to analyze. Many image segmentation methods are available but most of these methods are not suitable for satellite images and they require a priori knowledge. In order to overcome these obstacles, a new satellite image segmentation method is developed using an unsupervised artificial neural network method called Kohonen's self-organizing map and a threshold technique. Self-organizing map is used to organize pixels according to grey level values of multiple bands into groups then a threshold technique is used to cluster the image into disjoint regions, this new method is called TSOM. Experiments performed on two different satellite images confirm the stability, homogeneity, and the efficiency (speed wise) of TSOM method with comparison to the iterative self-organizing data analysis method. The stability and homogeneity of both methods are determined using a procedure selected from the functional model.

[1]  Bugao Xu,et al.  AUTOMATIC COLOR IDENTIFICATION IN PRINTED FABRIC IMAGES BY A FUZZY-NEURAL NETWORK , 2002 .

[2]  John R. Jensen,et al.  Introductory Digital Image Processing: A Remote Sensing Perspective , 1986 .

[3]  E. Nezry,et al.  Maximum A Posteriori Speckle Filtering And First Order Texture Models In Sar Images , 1990, 10th Annual International Symposium on Geoscience and Remote Sensing.

[4]  Satoshi Suzuki.  Graph-based vectorization method for line patterns , 1988, Proceedings CVPR '88: The Computer Society Conference on Computer Vision and Pattern Recognition.

[5]  Tamer Ölmez,et al.  Segmentation of remote-sensing images by incremental neural network , 2005, Pattern Recognit. Lett..

[6]  Kuldeep Kumar,et al.  Neural vs. statistical classifier in conjunction with genetic algorithm feature selection in digital mammography , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[7]  Christophe Collet,et al.  Unsupervised segmentation using a self-organizing map and a noise model estimation in sonar imagery , 2000, Pattern Recognit..

[8]  T. Zouagui,et al.  Image segmentation model , 2004 .

[9]  T. Poggio.  Vision by man and machine. , 1984, Scientific American.

[10]  M. V. Velzen,et al.  Self-organizing maps , 2007 .

[11]  J. Amini,et al.  Generalized Cooccurrence Matrix to Classify IRS-1D Images using Neural Network , 2004 .

[12]  Luigi Cinque,et al.  Run-Based Algorithms for Binary Image Analysis and Processing , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Xuewen Zhang,et al.  Remote sensing image segmentation based on self-organizing map at multiple-scale , 2007, Geoinformatics.

[14]  Hujun Yin,et al.  On the Distribution and Convergence of Feature Space in Self-Organizing Maps , 1995, Neural Computation.

[15]  Mohamad M. Awad,et al.  Multicomponent Image Segmentation Using a Genetic Algorithm and Artificial Neural Network , 2007, IEEE Geoscience and Remote Sensing Letters.

[16]  Jon Atli Benediktsson,et al.  Neural Network Approaches Versus Statistical Methods in Classification of Multisource Remote Sensing Data , 1989, 12th Canadian Symposium on Remote Sensing Geoscience and Remote Sensing Symposium,.

[17]  Neal R. Harvey,et al.  GENIE: a hybrid genetic algorithm for feature classification in multispectral images , 2000, SPIE Optics + Photonics.