A region-based GeneSIS segmentation algorithm for the classification of remotely sensed images

Remote Sensing

Volumn 7, Issue 3, 2015, Pages 2474-2508

  Mylonas, Stelios K ^a   Stavrakoudis, Dimitris G ^a   Theocharis, John B ^a   Mastorocostas, Paris A ^b  

^a ARISTOTLE UNIVERSITY OF THESSALONIKI   (Greece)

^b TECHNOLOGICAL EDUCATIONAL INSTITUTE OF SERRES   (Greece)

Author keywords

Genetic algorithms; Image segmentation; Marker selection; Object based classification; Segmentation fusion; Watershed transform

Indexed keywords

ALGORITHMS; EXTRACTION; FEATURE EXTRACTION; GENETIC ALGORITHMS; IMAGE PROCESSING; ITERATIVE METHODS; PIXELS; REMOTE SENSING; WATERSHEDS;

CLASSIFICATION ACCURACY; COMPUTATIONAL DEMANDS; MARKER SELECTION; OBJECT-BASED CLASSIFICATIONS; REMOTELY SENSED IMAGES; SEGMENTATION ALGORITHMS; SEQUENTIAL IMAGE SEGMENTATION; WATERSHED TRANSFORM;

IMAGE SEGMENTATION;

EID: 84926306117     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs70302474     Document Type: Article

References (33)

1. Fu, K.S.; Mui, J.K. A survey on image segmentation. Pattern Recogn. 1981, 13, 3-16.
2. Huang, X.; Zhang, L. An adaptive mean-shift analysis approach for object extraction and classification from urban hyperspectral imagery. IEEE Trans. Geosci. Remote Sens. 2008, 46, 4173-4185.
3. Yang, H.; Du, Q.; Ma, B. Decision fusion on supervised and unsupervised classifiers for hyperspectral imagery. IEEE Geosci. Remote Sens. Lett. 2010, 7, 875-879.
4. Beaulieu, J.; Goldberg, M. Hierarchy in picture segmentation: A stepwise optimal approach. IEEE Trans. Pattern Anal. 1989, 11, 150-163.
5. Baatz, M.; Schäpe, A. Multiresolution segmentation-An optimization approach for high quality multi-scale image segmentation. In Angewandte Geographische Informationsverarbeitung XII; Strobl, J., Blaschke, T., Griesbner, G., Eds.; Wichmann-Verlag: Heidelberg, Germany, 2000; pp. 12-23.
6. Tilton, J.C.; Tarabalka, Y.; Montesano, P.M.; Gofman, E. Best-merge region growing segmentation with integrated nonadjacent region object aggregation. IEEE Trans. Geosci. Remote Sens. 2012, 50, 4454-4467.
7. Hay, G.J.; Blaschke, T.; Marceau, D.J.; Bouchard, A. A comparison of three image-object methods for the multiscale analysis of landscape structure. ISPRS J. Photogramm. Remote Sens. 2003, 57, 327-345.
8. Benz, U.C.; Hofmann, P.; Willhauck, G.; Lingenfelder, I.; Heynen, M. Multi-resolution object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS J. Photogramm. Remote Sens. 2004, 58, 239-258.
9. Yu, Q.; Gong, P.; Clinton, N.; Biging, G.; Kelly, M.; Schirokauer, D. Object-based detailed vegetation classification with airborne high spatial resolution remote sensing imagery. Photogramm. Eng. Remote Sens. 2006, 72, 799-811.
10. Mallinis, G.; Koutsias, N.; Tsakiri-Strati, M.; Karteris, M. Object-based classification using Quickbird imagery for delineating forest vegetation polygons in a Mediterranean test site. ISPRS J. Photogramm. Remote Sens. 2008, 63, 237-250.
11. Tzotsos, A.; Karantzalos, K.; Argialas, D. Object-based image analysis through nonlinear scale-space filtering. ISPRS J. Photogramm. Remote Sens. 2011, 66, 2-16.
12. Feitosa, R.Q.; Costa, G.A.O.P.; Cazes, T.B.; Feijo, B. A genetic approach for the automatic adaptation of segmentation parameters. In Proceedings of the First International Conference on Object-Based Image Analysis, Salzburg, Austria, 4-5 July 2006.
13. Dragut, L.; Csillik, O.; Eisank, C.; Tiede, D. Automated parameterisation for multi-scale image segmentation on multiple layers. ISPRS J. Photogramm. Remote Sens. 2014, 88, 119-127.
14. Tarabalka, Y; Chanussot, J.; Benediktsson, J.A. Segmentation and classification of hyperspectral images using watershed transformation. Pattern Recogn. 2010, 43, 2367-2379.
15. Li, D.; Zhang, G.; Wu, Z.; Yi, L. An edge embedded marker-based watershed algorithm for high spatial resolution remote sensing image segmentation. IEEE Trans. Image Process. 2010, 19, 2781-2787.
16. Angulo J.; Velasco-Forero S. Semi-supervised hyperspectral image segmentation using regionalized stochastic watershed. Proc. SPIE 2010, 7695, doi:10.1117/12.850187.
17. Derivaux, S.; Forestier, G.; Wemmert, C.; Lefevre, S. Supervised image segmentation using watershed transform, fuzzy classification and evolutionary computation. Pattern Recogn. Lett. 2010, 31, 2364-2374.
18. Pratikakis, I.; Vanhammel, I.; Sahli, H.; Gatos, B.; Perantonis S.J. Unsupervised watershed-driven region-based image retrieval. Vis. Image Signal Process. 2006, 153, 313-322.
19. Makrogiannis, S.; Economou, G.; Fotopoulos, S. Region dissimilarity relation that combines feature-space and spatial information for color image segmentation. IEEE Trans. Syst. Man Cy. B 2005, 35, 44-53.
20. Mylonas, S.K.; Stavrakoudis, D.G.; Theocharis, J.B. GeneSIS: A GA-based fuzzy segmentation algorithm for remote sensing images. Knowl. Based Syst. 2013, 54, 86-102.
21. Mylonas, S.K.; Stavrakoudis, D.G.; Theocharis, J.B; Mastorocostas, P.A. Spectral-spatial classification of remote sensing images using a region-based GeneSIS segmentation algorithm. In Proceedings of the IEEE International Conference on Fuzzy Systems, Beijing, China, 6-11 July 2014; pp. 1976-1984.
22. Moustakidis, S.P.; Mallinis, G.; Koutsias, N.; Theocharis, J.B. SVM-based fuzzy decision trees for classification of high spatial resolution remote sensing images. IEEE Trans. Geosci. Remote Sens. 2012, 50, 149-169.
23. Evans, A.N.; Liu, X.U. A morphological gradient approach to color edge detection. IEEE Trans. Image Process. 2006, 15, 1454-1463.
24. Meyer, F. Topographic distance and watershed lines. Signal. Process. 1994, 38, 113-125.
25. Cho, S.-B.; Kim, J.H. Multiple network fusion using fuzzy logic. IEEE Trans. Neural Networks 1995, 6, 497-501.
26. Tarabalka, Y.; Tilton, J.C.; Benediktsson, J.A.; Chanussot, J. A marker-based approach for the automated selection of a single segmentation from a hierarchical set of image segmentations. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2012, 5, 262-272.
27. Herrera, F.; Lozano, M.; Sanchez, A.M. A taxonomy for the crossover operator for real-coded genetic algorithms: an experimental study. Int. J. Intell. Syst. 2003, 18, 309-338.
28. Tarabalka, Y.; Chanussot, J.; Benediktsson, J.A. Segmentation and classification of hyperspectral images using minimum spanning forest grown from automatically selected markers. IEEE Trans. Syst. Man. Cy. B 2010, 40, 1267-1279.
29. Tarabalka, Y.; Benediktsson, J.A.; Chanussot, J.; Tilton, J.C. Multiple spectral-spatial classification approach for hyperspectral data. IEEE Trans. Geosci. Remote Sens. 2010, 48, 4122-4132.
30. Tarabalka, Y.; Tilton, J.C. Spectral-spatial classification of hyperspectral images using hierarchical optimization. In Proceedings of Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, Lisbon, Portugal, 6-9 June 2011; pp. 1-4.
31. Tarabalka, Y.; Tilton, J.C. Best merge region growing with integrated probabilistic classification for hyperspectral imagery. In Proceedings of the International Geoscience and Remote Sensing Symposium, Vancouver, BC, Canada, 24-29 July 2011; pp. 3724-3727.
32. Tadjudin, S.; Landgrebe, D.A. Covariance estimation with limited training samples. IEEE Trans. Geosci. Remote Sens. 1999, 37, 2113-2118.
33. Mitrakis, N.E.; Topaloglou, C.A.; Alexandridis, T.K.; Theocharis, J.B.; Zalidis, G.C. Decision fusion of GA self-organizing neuro-fuzzy multilayered classifiers for land cover classification using textural and spectral features. IEEE Trans. Geosci. Remote Sens. 2008, 46, 2137-2152.