Bhandarkar, S.M., and H. Zhang, 1999. Image Segmentation using
evolutionary computation,
IEEE Transactions on Evolutionary
Computation
, 3(1):1–21.
Bhattacharyya, A., 1943. On a measure of divergence between two
statistical populations defined by their probability distributions,
Bulletin of the Calcutta Mathematical Society
, 35(1):99–109.
Bitam, A., and S. Ameur, 2013. A local-spectral fuzzy segmentation
for MSG multispectral images,
International Journal of Remote
Sensing
, 34(23):8360–8372.
Brown, G., A. Pocock, M. Lujan, and M.J. Zhao, 2012. Conditional
likelihood maximisation: A unifying framework for information
theoretic feature selection,
Journal of Machine Learning
Research
, 13(1):27–66.
Cai-hong, M., D. Qin, L. and Shi-Bin, 2012. A hybrid PSO-ISODATA
algorithm for remote sensing image segmentation,
Proceedings
of the International Conference on Industrial Control and
Electronics Engineering (ICICEE12)
, pp. 1371–1375.
Cannon, R.L., J.V. Dave, J.C. Bezdek, and M.M. Trivedi, 1986.
Segmentation of a Thematic Mapper Image using the Fuzzy
C-Means clustering algorithm,
IEEE Transactions on Geoscience
and Remote Sensing,
24 (3):400–408.
Cheng, H., Z. Qin, C. Feng, Yong Wang, and F. Li, 2011. Conditional
mutual information-based feature selection analyzing for synergy
and redundancy,
Electronics and Telecommunications Research
Institute (ETRI) Journal
, 33(2):210–220.
Clausi, D.A., and H. Deng, 2005.Design-based texture feature fusion
using Gabor filters and co-occurrence probabilities,
IEEE
Transactions on Image Processing
,
14(7):925–936.
Congalton, R.G., 2010, Remote sensing: An overview,
GIScience &
Remote Sensing
, 47(4):443–459.
Das, S., A. Konar, and U.K. Chakraborty, 2006. Automatic
Fuzzy
Segmentation of Images with Differential Evolution
,
Proceedings
of the IEEE Congress on Evolutionary Computation (CEC-2006)
,
pp. 2026–2033.
Digital Globe, URL:
-
information
(last date accessed: 06 January 2016).
Fleuret, F., 2004. Fast binary feature selection with conditional
mutual information,
Journal of Machine Learning Research
,
5(4941):1531–1555.
Gao, Y.,J.F. Mas, N. Kerle, and A.N. Pacheco, 2011. Optimal region
growing segmentation and its effect on classification accuracy,
International Journal of Remote Sensing
, 32(13):3747–3763.
Gen-yuan, D., M. Fang, T. Sheng-li, and L. Ye, 2009. A modified
fuzzy C-means algorithm in remote sensing image segmentation,
Proceedings of the International Conference on Environmental
Science and Information Application Technology
.
Haralick, R.M., K. Shanmugan, and I. Dinstein, 1973. Texture features
for image classification,
IEEE Transactions on Systems, Man and
Cybernetics
,
3(6):610–621.
Hung, C., W. Liu, and B. Kuo, 2008. A new adaptive fuzzy clustering
algorithm for remotely sensed images,
Proceedings of the IEEE
Geoscience and Remote Sensing Symposium
, 2(2008):863–866.
Jakulin, A., 2005.
Machine Learning Based on Attribute Interactions
,
Ph.D. dissertation, University of Ljubljana.
Kira, K., and L. Rendell., 1992. The feature selection problem:
Traditional methods and a new algorithm,
Proceedings of AAAI-
92
, AAAI Press, pp. 129–134.
Lewis, D.D., 1992. Feature selection and feature extraction for text
categorization,
Proceedings of the Workshop on Speech and
Natural Language
, Association for Computational Linguistics,
Morristown, New Jersey, pp. 212–217.
Lin, D., and X. Tang, 2006. Conditional infomax learning: An
integrated framework for feature extraction and fusion,
Proceedings of ECCV, 1(2006):68–82.
Lizarazo, I., and J. Barros, 2010. Fuzzy image segmentation for urban
land-cover classification,
Photogrammetric Engineering &
Remote Sensing
, 76(2):151–162.
Liu, J., and Y.H. Yang, 1994. Multi-resolution color image
segmentation,
IEEE Transactions on Pattern Analysis and
Machine Intelligence,
16(7):689–700.
Meyer, P., and G. Bontempi, 2006. On the use of variable
complementarily for feature selection in cancer classification,
Evolutionary Computation and Machine Learning in
Bioinformatics
, pp. 91–102.
Mitra, P., C.A. Murthy,and S.K. Pal, 2002. Unsupervised feature
selection using feature similarity,
IEEE Transactions on Pattern
Analysis and Machine Intelligence
, 24(3):301–312.
Mitra, P., B.U. Shankar, and S.K. Pal, 2004. Segmentation of
multispectral remote sensing images using active support vector
machines,
Pattern Recognition Letters
, 25(2004):1067–1074.
Moustakidis, S., G. Mallinis, N. Koutsias, J.B. Theocharis,and V.
Petridis, 2012. SVM-based fuzzy decision trees for classi cation
of high spatial resolution remote sensing images,
IEEE
Transactions on Geoscience and Remote Sensing
, 50(1):149–169.
Peng, H., F. Long, and C. Ding, 2005. Feature selection based on
mutual information: Criteria of max-dependency, max-relevance,
and min-redundancy,
IEEE Transactions on Pattern Analysis and
Machine Intelligence
, 27(8):1226–1238.
Rekik, A., M. Zribi, M. Benjelloun, and Ahmed ben Hamida, 2006.
A k-Means clustering algorithm initialization for unsupervised
statistical satellite image segmentation,
Proceedings of the
E-Learning in Industrial Electronics
, Hammamet,Tunisia.
Rekik, A., M. Zribi, A. Hamida, and M. Benjelloun, 2009. An optimal
unsupervised satellite image segmentation approach based on
Pearson system and k-means clustering algorithm initialization,
International Journal of Signal Processing
, 5(8):38–45.
Richards, J.A., 2013.
Remote Sensing Digital Image Processing: An
Introduction
, Fourth edition, Springer, ISBN 3-540-25128-6.
Sengur, A., I. Turkoglu, and M. Cevdetince, 2008. Wavelet oscillator
neural networks for texture segmentation,
Neural Network
World
, 4(8):275–289.
Shannon, C.E., 1948. A mathematical theory of communication,
Bell
Systems Technical Journal
, 27(3):379–423.
Tilton, J.C., 1998. Image Segmentation by region growing and spectral
clustering with a natural convergence criterion,
Proceedings of
IGARSS ‘98
, Seattle, Washington, 06-10 July, 4(1998):1766–1768.
Tilton, J., D. Cook, and N. Ketkarm, 2008. The integration of graph
based knowledge discovery with image segmentation hierarchies
for data analysis, data mining and knowledge discovery,
Proceedings of the IEEE International Geoscience and Remote
Sensing Symposium
.
Tou, J.T., and R.C. Gonzalez, 1974.
Pattern Recognition Principles
,
Addison-Wesley Publishing Company, Reading: Massachusetts.
Tuceryan, M., and A.K. Jain, 1998. Texture analysis,
The Handbook
of Pattern Recognition and Computer Vision
, Second edition,
World Scientific Publishing Company, pp. 207-248.
Visalakshi, N.K., and K. Thangavel, 2009. Impact of normalization
in distributed K-means clustering,
International Journal of Soft
Computing
, 4(4):168–172.
Xie, F., D. Chen, J. Meligrana, Y. Lin, and W. Ren, 2013. Selecting
key features for remote sensing classification by using decision-
theoretic rough set model,
Photogrammetric Engineering &
Remote Sensing
, 79(9):787–797.
Yang, H., and J. Moody, 1999. Data visualization and feature selection:
New algorithms for non-Gaussian data,
Advances in Neural
Information Processing Systems
, 12(1999):687–693.
Yu, L., and H. Liu, 2004. Efficient feature selection via analysis
of relevance and redundancy,
Journal of Machine Learning
Research
, 5(2004):1205–1224.
Zhang, H., J.E. Fritts, S.A. Goldman, 2008. Image segmentation
evaluation: A survey of unsupervised methods,
Computer Vision
and Image Understanding
, 110(2008):260–268.
Zhang, X., X. Yang, P. Chen, and L. Jiao, 2008. A complete
unsupervised learning of mixture models for texture image
segmentation,
Proceedings of the Congress on Image and Signal
Processing
, 2(2008):605–609.
(Received 28 January 2015; accepted 12 May 2015; final ver-
sion 26 September 2015)
222
March 2016
PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING