Open Access Open Access  Restricted Access Subscription or Fee Access

Texture Features Extraction and Backtracking Search Optimization Algorithm for Satellite Image Clustering

Nourredine Boudali(1*), Hadria Fizazi(2), Meriem Abidi(3)

(1) University of Science and Technology of Oran, Mohamed Boudiaf, Algeria
(2) University of Science and Technology of Oran, Mohamed Boudiaf, Algeria
(3) University of Science and Technology of Oran, Mohamed Boudiaf, Algeria
(*) Corresponding author


DOI: https://doi.org/10.15866/irease.v12i5.15224

Abstract


Clustering is one of the data analysis methods used in image processing. It consists in dividing all the pixels of an image into a corresponding class pixel by pixel. In this paper, the Backtracking Search Algorithm (BSA) has been investigated in order to cluster this heterogeneous set of information using a combination of different textural features. The validity index is used to determine the appropriate number of classes representing an image and to evaluate the results of the classification algorithm. The study realized with Calinski Harabasz (CHI) and Davies-Bouldin Index (DBI) allows finding the best validity index to evaluate fitness function. The experiences obtained show the effectiveness and performance of the Davies-Bouldin Index (DBI) with BSA algorithm.
Copyright © 2019 Praise Worthy Prize - All rights reserved.

Keywords


Backtracking Search Optimization; Textural Feature; Unsupervised Clustering; Validity Index

Full Text:

PDF


References


N. E. A. Khalid, N. M. Ariff, S. Yahya, and N. M. Noor, A review of bio-inspired algorithms as image processing techniques, in International Conference on Software Engineering and Computer Systems, pp. 660–673, (2011).
https://doi.org/10.1007/978-3-642-22170-5_57

Hannane, A., Fizazi, H., Metaheuristics and Neural Network for Satellite Images Classification, (2016) International Review of Aerospace Engineering (IREASE), 9 (4), pp. 107-113.
https://doi.org/10.15866/irease.v9i4.10220

Si Tayeb, M., Fizazi, H., A Dual-Level Hybrid Approach for Classification of Satellite Images, (2017) International Review of Aerospace Engineering (IREASE), 10 (1), pp. 42-49.
https://doi.org/10.15866/irease.v10i1.11191

H. Tong, M. Zhao, and X. Li, Applications of computational intelligence in remote sensing image analysis, in International Symposium on Intelligence Computation and Applications, (2009), pp. 171–179.
https://doi.org/10.1007/978-3-642-04962-0_20

L. Goel, D. Gupta, V. K. Panchal, and A. Abraham, Taxonomy of nature inspired computational intelligence: A remote sensing perspective, in Proceedings of the 4th World Congress on Nature and Biologically Inspired Computing, NaBIC 2012, pp. 200–206.
https://doi.org/10.1109/nabic.2012.6402262

Y. Long, Y. Gong, Z. Xiao, and Q. Liu, Accurate object localization in remote sensing images based on convolutional neural networks. IEEE Transactions on Geoscience and Remote Sensing, vol. 55, no.5, (2017), pp .2486-2498.
https://doi.org/10.1109/tgrs.2016.2645610

E. Li et al., Integrating multilayer features of convolutional neural networks for remote sensing scene classification, IEEE Transactions on Geoscience and Remote Sensing, vol. 55, no.10, (2017), pp. 5653-5665.
https://doi.org/10.1109/tgrs.2017.2711275

H. Mahi, M. Kaouadji, Shape-texture features for the VHSR satellite images classification using the MLP neural net, EARSeL eProceedings, vol. 13, no. 2,( 2014), p. 67.

M. M. Awad and K. Chehdi, Satellite image segmentation using hybrid variable genetic algorithm, Int. J. Imaging Syst. Technol, vol. 19, no. 3, (2009), pp. 199–207.
https://doi.org/10.1002/ima.20195

S. Bandyopadhyay, S. K. Pal, Pixel classification using variable string genetic algorithms with chromosome differentiation, IEEE Trans. Geosci Remote Sens, vol. 39, no. 2, (2001), pp. 303–308.
https://doi.org/10.1109/36.905238

T. Lucas, T. C. Silva, R. Vimieiro, et al., A new evolutionary algorithm for mining top-k discriminative patterns in high dimensional data. Applied Soft Computing, vol. 59, (2017), pp. 487-499.
https://doi.org/10.1016/j.asoc.2017.05.048

P. Tangpattanakul, N. Jozefowiez, et al, A multi-objective local search heuristic for scheduling Earth observations taken by an agile satellite, European Journal of Operational Research, vol.245, no. 2, 2015, pp. 542-554.
https://doi.org/10.1016/j.ejor.2015.03.011

M. Sama, et al., Ant colony optimization for the real-time train routing selection problem. Transportation Research Part B: Methodological, vol.85, (2016), pp. 89-108.
https://doi.org/10.1016/j.trb.2016.01.005

Aabid, M., Elakkary, A., Sefiani, N., PID Parameters Optimization Using Ant-Colony Algorithm for Human Heart Control, (2017) International Review on Modelling and Simulations (IREMOS), 10 (2), pp. 94-102.
https://doi.org/10.15866/iremos.v10i2.11230

Benchennane, I., Hadjar, A., Benyettou, A., Individuals Identification Using Artificial Immunes Systems, (2015) International Review on Computers and Software (IRECOS), 10 (1), pp. 20-26.
https://doi.org/10.15866/irecos.v10i1.4560

P. Civicioglu, Backtracking Search Optimization Algorithm for numerical optimization problems, Appl. Math. Comput, vol. 219, no 15, (2013), pp 8121–8144.
https://doi.org/10.1016/j.amc.2013.02.017

O. Arbelaitz, I. Gurrutxaga, J. Muguerza, J. M. PéRez, I. Perona, An extensive comparative study of cluster validity indices, Pattern Recognit, vol. 46, no. 1, (2013), pp. 243–256.
https://doi.org/10.1016/j.patcog.2012.07.021

D. Lam, M. Wei and D. Wunsch, Clustering data of mixed categorical and numerical type with unsupervised feature learning, IEEE Access, vol. 3, (2015), pp. 1605-1613.
https://doi.org/10.1109/access.2015.2477216

P. H. Thong, A novel automatic picture fuzzy clustering method based on particle swarm optimization and picture composite cardinality, Knowledge-Based Systems, vol. 109, (2016), pp. 48-60.
https://doi.org/10.1016/j.knosys.2016.06.023

S. Saha, A. K. Alok, A. Ekbal, Brain image segmentation using semi-supervised clustering. Expert Systems with Applications, vol. 52, (2016), pp. 50-63.
https://doi.org/10.1016/j.eswa.2016.01.005

K. Raval, R. Shukla, A. Shah, Color Image Segmentation Using Optimized FCM Based on Modified Xie-Beni’s Validity Index. Journal of Image Processing & Pattern Recognition Progress, vol. 4, no. 2, (2017), pp. 4-12.

M. S. Yang, Y. Nataliani, Robust-learning fuzzy c-means clustering algorithm with unknown number of clusters. Pattern Recognition, vol. 71, (2017), pp. 45-59.
https://doi.org/10.1016/j.patcog.2017.05.017

D. L. Davies, D. W. Bouldin, A cluster separation measure, IEEE Trans. Pattern Anal. Mach. Intell., no. 2, (1979), pp. 224–227.
https://doi.org/10.1109/tpami.1979.4766909

P. N. Banu, S. Andrews, Performance analysis of hard and soft clustering approaches for gene expression data. International Journal of Rough Sets and Data Analysis (IJRSDA), vol. 2, no.1, (2015), pp. 58-69.
https://doi.org/10.4018/ijrsda.2015010104

T. Caliński, J. Harabasz, A dendrite method for cluster analysis, Commun. Stat. Methods, vol. 3, no. 1, (1974), pp. 1-27.

G. Chen, S. Jaradat, N. Banerjee, T. Tanaka, M. Ko, M. Zhang, Evaluation and comparison of clustering algorithms in analyzing es cell gene expression data, Stat. Sin, vol. 12, no. 1, (2002), pp. 241–262.

J. Mridula, Feature Based Segmentation of Colour Textured Images using Markov Random Field Model, Master of Technology, vol. 5, no, (2011), pp. 1–122.

R. M. Haralick, K. Shanmugam, Textural features for image classification, IEEE Trans. Syst. Man. Cybern., no. 6, (1973), pp. 610–621.
https://doi.org/10.1109/tsmc.1973.4309314

Y. Sheoran, V. Kumar, K. P. S. Rana, P. Mishra, J. Kumar, and S. S. Nair, Development of Backtracking Search Optimization Algorithm Toolkit in LabVIEWTM, Procedia Comput. Sci., vol. 57, (2015), pp. 241–248.
https://doi.org/10.1016/j.procs.2015.07.476

H. Li, S. Zhang, X. Ding, C. Zhang, P. Dale, Performance evaluation of cluster validity indices (CVIs) on multi/hyperspectral remote sensing datasets, Remote Sens., vol. 8, no. 4,( 2016), pp. 295.
https://doi.org/10.3390/rs8040295

http://cs.uef.fi/sipu/datasets

http://pages.upf.pf/Sebastien.Chabrier/ressources.php

https://earthexplorer.usgs.gov


Refbacks

  • There are currently no refbacks.



Please send any question about this web site to info@praiseworthyprize.com
Copyright © 2005-2021 Praise Worthy Prize