Object-based multispectral image segmentation and classification

2014 7th International Symposium on Telecommunications, IST 2014

Volumn , Issue , 2014, Pages 430-435

  Mirzapour, Fardin ^a   Ghassemian, Hassan ^a  

^a TARBIAT MODARES UNIVERSITY   (Iran)

Author keywords

Image compression; Multispectral Images; Object based Classification; Remote Sensing; Segmentation

Indexed keywords

DIGITAL IMAGE STORAGE; IMAGE CLASSIFICATION; IMAGE COMPRESSION; PIXELS; REMOTE SENSING; SPACE OPTICS;

CLASSIFICATION PERFORMANCE; MULTISPECTRAL IMAGES; OBJECT-BASED CLASSIFICATIONS; OBJECT-BASED METHODS; PARTIAL DIFFERENTIAL EQUATIONS (PDE); SCENE REPRESENTATION; STATISTICAL REGION MERGING; UNSUPERVISED SEGMENTATION;

IMAGE SEGMENTATION;

EID: 84931035556     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ISTEL.2014.7000742     Document Type: Conference Paper

References (14)

1. H. Ghassemian, "On-Board Satellite image Compression by Object feature Extraction," In Proceeding of ISPRS-2004, vol.3, pp. 820-825, Commission 3, July 2004.
2. R. L. Kettig and D. A. Landgrebe, "Classification of Remotely Sensed Multispectral Image Data by Extraction and Classification of Homogeneous Objects," IEEE Transaction on Geoscience and Remote Sensing, vol. GE-39, No. 1, pp. 4-16, 1976.
3. H. Ghassemian and D. Landgrebe, "Multispectral Image Compression by an On-Board Scene Segmentation," In Proceeding of IEEE International Geoscience and Remote Sensing Symposium. Scanning the Present and Resolving the Future, Sydney Australia, July 2001.
4. R. Nock and F. Nielsen, "Statistical region merging," IEEE transction on pattern analysis and machine intelligence, vol.26, no.11, 2004.
5. P. Perona and J. Malik, "Scale-space and edge detection using anisotropic diffusion," IEEE Transaction on Pattern Analysis and Machine Intelligence, vol. 12, no. 7, pp. 629-639, Jul. 1990.
6. J. M. Duarte-Carvajalino, Pa. E. Castillo, M. Velez-Reyes, "Comparative Study of Semi-Implicit Schemes for Nonlinear Diffusion in Hyperspectral Imagery," IEEE Transaction on Image Processing, vol. 16, no. 5, pp.1303-1314, 2007.
7. J. Weickert, B. M. T. H. Romeny, and M. A. Viergever, "Efficient and reliable schemes for nonlinear diffusion filtering," Image Processing, IEEE Transactions on, vol. 7, pp. 398-410, 1998.
8. S. Bernabe, P. Reddy Marpu, A. Plaza, M. Dalla Mura, and J. Atli Benediktsson, "Spectral-Spatial Classification of Multispectral Images Using Kernel Feature Space Representation," Geoscience and Remote Sensing Letters, IEEE, vol. 11, pp. 288-292, 2014.
9. Z. Penglin, L. Zhiyong, and S. Wenzhong, "Object-Based Spatial Feature for Classification of Very High Resolution Remote Sensing Images," Geoscience and Remote Sensing Letters, IEEE, vol. 10, pp. 1572-1576, 2013.
10. M. Khodadadzadeh, J. Li, A. Plaza, H. Ghassemian, J. M. Bioucas-Dias, and X. Li, "Spectral-Spatial Classification of Hyperspectral Data Using Local and Global Probabilities for Mixed Pixel Characterization," Geoscience and Remote Sensing, IEEE Transactions on, vol. Accepted: 2014, 2014.
11. F. Nielsen and R. Nock, "On region merging: the statistical soundness of fast sorting with applications," In Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, IEEE Computer Society Press, Silver Spring, MD, pp.19-27, 2003.
12. H. T. Li, H.Y. Gu, Y. S. Han, J. H. Yang, "An efficient multi-scale segmentation for high-resolution remote sensing imagery based on Statistical region merging and minimum heterogeneity rule," IEEE International Workshop on Earth Observation and Remote Sensing Applications, pp.1-6, EORSA 2008.
13. Chih-Chung Chang and Chin-Jen Lin, "LIBSVM : a library for support vector machines," ACM Trans. on Intel. Sys. and Tech., vol. 2, no. 3, pp. 27:1-27:27, 2011, Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.
14. G. M. Foody, "Thematic map comparison: evaluating the statistical significance of differences in classification accuracy," Photogrammetric engineering and remote sensing, vol. 70, pp. 627-634, 2004.