An evolutionary computing frame work toward object extraction from satellite images

Egyptian Journal of Remote Sensing and Space Science

Volumn 16, Issue 2, 2013, Pages 163-169

  Arun, P V ^a   Katiyar, S K ^a  

^a MAULANA AZAD NATIONAL INSTITUTE OF TECHNOLOGY   (India)

Author keywords

Cellular neural network; Feature detection; Interpolation

Indexed keywords

CELLULAR NEURAL NETWORKS; INTERPOLATION;

ADAPTIVE KERNELS; CONTEXTUAL KNOWLEDGE; EVOLUTIONARY COMPUTING; FEATURE DETECTION; IMAGE INTERPRETATION; INTELLIGENT INTERPRETATION; OBJECT EXTRACTION; TRADITIONAL APPROACHES;

FEATURE EXTRACTION;

ARTIFICIAL NEURAL NETWORK; IMAGE ANALYSIS; INTERPOLATION; SATELLITE IMAGERY;

EID: 84892802876     PISSN: 11109823     EISSN: 20902476     Source Type: Journal    
DOI: 10.1016/j.ejrs.2013.05.003     Document Type: Article

References (31)

1. P.K. Agarwal, B. Aronov, and M. Sharir Exact and approximation algorithms for minimum-width cylindrical shells Discrete Computational Geometry 26 3 2001 307 320
2. Back, T.; and Ron, B.; 2005. Using genetic algorithms to evolve behavior in cellular automata, Proceedings of the 4th international conference on Unconventional Computation, 3699 (3), 1-10. (Pubitemid 41491488)
3. M. Badoiu, S. Har-Peled, and P. Indyk Approximate clustering via Coreset Proceedings of 34th Annual ACM Symposium, Theory of Computation 2002 250 257
4. A. Carlos, Aguirre-Salado, J. Eduardo, Treviño-Garza, A. Oscar, Aguirre-Calderón, Javier Jiménez-Pérez, A. Marco, González-Tagle, J. Liliana Miranda-Aragón, René Valdez-Lazalde, I. Alejandro, and Guillermo Sánchez-Díaz. Aguirre-Salado Forest cover mapping in North-Central Mexico: a comparison of digital image processing methods GIScience & Remote Sensing 49 6 2012 895 914
5. T. Chang, and C.J. Kuo Texture analysis and classification with tree-structured wavelet transform IEEE Transactions on Image Processing 2 4 2006 429 441
6. K.C. Chi Surface registration using a dynamic genetic algorithm Journal of Pattern Recognition 37 1 2004 105 117
7. R.G. Congalton A review of assessing the accuracy of classifications of remotely sensed data Remote Sensing of Environment 37 1 1991 35 46
8. Daniel, A.L.; Gang Hong, Yun Zhang, 2006. Performance assessment of automated feature extraction tools on high resolution imagery, MAPPS/ASPRS Conference, American Society of Photogrammetry and Remote Sensing, San Antonio, Texas November 2006.
9. R.M. Haralick, L.T. Watson, and T.J. Laffey The topographic primal sketch International Journal of Robotics Research 2 1 1983 50 72
10. M. Harvey User centric evaluation of semi automated feature extraction from remote sensing images IGARSS 23 2008 234 240
11. R.S. Hosseini, and S. Homayoun A SVMS-based hyper spectral data classification algorithm in a similarity space Workshop on Hyper spectral Image and Signal Processing: Evolution in Remote Sensing - WHISPERS 09 21 2 2009 1 4
12. R. Jenssen, K.E. Hild, D. Erdogmus, J.C. Principe, and T. Eltoft Clustering Using Renyi's Entropy. Proc. International Joint Conference on Neural Networks 2003 Portland USA pp. 523-528
13. S. Kumar, and M. Hebert Discriminative random fields: a discriminative framework for contextual interaction in classification Proceedings of Ninth IEEE International Conference on Computer Vision 2 4 2003 1150 1157
14. Z. Lari, and H. Ebadi Automated building extraction from high-resolution satellite imagery using spectral and structural information based on artificial neural networks International Conference on Object Extraction 21 2 2011 23 27
15. C. Lee, M. Schmidt, and R. Greiner Support Vector Random Fields for Spatial Classification". 9th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD) 2005 Portugal USA p. 196
16. J.Q. Lindi A review of techniques for extracting linear features from imagery Photogrammetric Engineering & Remote Sensing 70 12 2004 1383 1392
17. Marneffe, M.; MacCartney, B.; Manning, C.; 2006. Generating typed dependency parses from phrase structure parses. Proceedings of 5th International Conference on Language Resources and Evaluation (LREC 2006), 24-26th May, Genoa, Italy, pp. 449-454.
18. M. Mitchell, J.P. Crutchfield, and R. Das Evolving cellular automata with genetic algorithms: a review of recent work First International Conference on Evolutionary Computation and Its Applications (EvCA'96) 1 1 1996 120 130
19. Carol S. Mladinich An evaluation of object-oriented image analysis techniques to identify motorized vehicle effects in semi-arid to arid ecosystems of the American West GIScience & Remote Sensing 47 1 2010 53 77
20. C. Orovas, and J. Austin A cellular system for pattern recognition using associative neural networks IEEE International Workshop on Cellular Neural Networks and their Application 2 5 1998 11
21. J. Porway, K. Wang, B. Yao, and S.C Zhu A hierarchical and contextual model for aerial image understanding IEEE Computer Society Conference on Computer Vision and Pattern Recognition 49 7 2008 155 160
22. Pengcheng Qi, Chuanyan Zhao, and Zhaodong Feng GIS- and machine learning - based modeling of the potential distribution of broadleaved deciduous forest in the Chinese Loess Plateau GIScience & Remote Sensing 47 1 2010 99 114
23. P. Schnitzspan, F. Mario, and S. Bernt Hierarchical support vector random fields: joint training to combine local and global features Computer Vision - ECCV 5303 5 2008 527 540
24. Sikdar, B.K.; Paul, K.; Biswas, G.P.; Yang, C.; Bopanna, V.; Mukherjee, S.; Chaudhuri, P.P.; 2000. Theory and application of GF(2P) cellular automata as on-chip test pattern generator. Proceedings of 13th International Conference on VLSI Design India 556-561. (Pubitemid 30573321)
25. Srivastava, A. N.; 2004. Mixture density mercer kernels: a method to learn kernels directly from data. SIAM Data Mining Conference, Lake Buena Vista, Florida, USA: Florida University. 22-24 April, 369-378.
26. Roy S. Stine, Debasish Chaudhuri, Peter Ray, Prasad Pathak, and Mary Hall-Brown Comparison of digital image processing techniques for classifying Arctic Tundra GIScience & Remote Sensing 47 1 2010 78 98
27. Sunil, R.R.; Dennis, D.T.; Eric, K.; Charles, G.O.; 2004. Comparing Spectral and Object Based Approaches for Classification and Transportation Feature Extraction from High Resolution Multispectral Imagery. ASPRS Annual Conference Proceedings, Denver: Colorado.
28. V. Vapnik Statistical Learning Theory 1998 Wiley Publishers Inc. New York pp. 230-240
29. H. Wang, Y. Yang, S. Ma, and C. Guo Automatic object extraction based on fuzzy mask Intelligent Systems and Applications, ISA International Workshop On 23 5 2009 1 4
30. J. Yuan Automatic road extraction from satellite imagery using LEGION networks Proceedings of International Joint Conference on Neural Networks 45 2 2009 120
31. Daxiang. Zhang, Chuanrong. Zhang, Robert. Cromley, David. Travis, and Daniel Civco An object-based method for contrail detection in AVHRR satellite images GIScience & Remote Sensing 49 3 2012 412 427