Texture feature extraction for land-cover classification of remote sensing data in land consolidation district using semi-variogram analysis

WSEAS Transactions on Computers

Volumn 7, Issue 7, 2008, Pages 857-866

  Huang, Yan ^a   Yue, Anzhi ^a   Wei, Su ^a   Li, Daoliang ^a   Luo, Ming ^b   Jiang, Yijun ^b   Zhang, Chao ^a  

^a CHINA AGRICULTURAL UNIVERSITY   (China)

^b Chongqing Institute of Geology and Mineral Resources   (China)

Author keywords

Classification; Land consolidation; Semi variogram; Texture feature

Indexed keywords

IMAGE CLASSIFICATION; IMAGE ENHANCEMENT; IMAGE RECONSTRUCTION; LANDFORMS; REMOTE SENSING; SECURITY OF DATA; SPACE OPTICS; TEXTURES; WATER ANALYSIS; WINDOWS;

CLASSIFICATION; LAND CONSOLIDATION; SEMI-VARIOGRAM; TEXTURE FEATURE;

FEATURE EXTRACTION;

EID: 53449091261     PISSN: 11092750     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (28)

1. Chunfang, K., Kai, X., Chonglong, W. Classification and Extraction of Urban Land-Use Information from High-Resolution Image Based on Object Multi-features, Journal of China University of Geosciences, Vol. 17, No. 2, 2006, pp. 151-157.
2. Haralick, R. M., Statistical and Structural Approaches to Texture, Proceedings of the IEEE, Vol. 67, No. 5, 1979, pp.786-804.
3. Ruiz, L. A., Fdez-Sarria, A., Recio, J. A., Texture feature extraction for classification of remote sensing data using wavelet decomposition: a comparative study, 20th ISPRS Congress, 2004.
4. Laine, A., Fan, J., Texture classification by wavelet packet signatures, IEEE Trans. on PAMI, Vol. 15, No. 11, 1993, pp.1186-1191.
5. Volker, M., Christoph, P., Thomas, L., Til, A., Texture classification of gray level images by multiscale cross co-occurrence matrices, Pattern Recognition, Vol. 2, 2000, pp. 549-552.
6. Curran, P. J., The semivarigram in remote sensing: an introduction, Remote Sens of Environ, Vol.24, No.3, 1988, pp.493-507.
7. Journel, A. G., Huijbregts, Ch. J., Mining geostatistics, Academic Press, 1978.
8. Sevkiye, S. T., An analysis on the efficient applicability of the land readjustment (LR) method in Turkey, Habitat International, Vol. 31, No. 1, 2007, pp. 53-64.
9. Haralick, R. M., Texture features for image classification, IEEE Trans SMC, Vol.3, No.6, 1973, pp. 610-621.
10. Chuen-Lin, T., You-Ru, L., Shiao-Shan, J., Surface flatness of optical thin films evaluated by gray level co-occurrence matrix and entropy, Applied Surface Science, In Press, Corrected Proof, Available online 26 January 2008.
11. Mirowski, P., Daniel, M., Tetzlaff, Retrieving scale from quasi-stationary images, Pattern Recognition Letters, Vol. 29, No. 6, 2008, pp. 754-767.
12. Randen, T., Husoy, J. H., Filtering for Texture Classification: A Comparative Study, IEEE Trans. PAMI, Vol. 21, No. 4, 1999, pp. 291-310.
13. Brooker, P. I., Changes in dispersion variance consequent upon inaccurately modelled semi-variograms, Mathematics and Computers in Simulation, Vol.30, No.1-2, 1988, pp. 11-16.
14. Odorico, P. D., Ridolfi, I., Porporato, A., et al, Preferential states of seasonal soil moisture: the impact of climate fluctuations, Water Resources Research, Vol. 36, No. 8, 2000, pp. 2209-2211.
15. Altese, E., Bolognani, O., Mancini, M., et al, Retrieving soil wetness over soil from ESRI synthetic aperture radar data: sensitivity analysis based on a theoretical surface scattering model and field data, Water Resources Research, Vol. 32, No.3, 1996, pp. 653-661.
16. Hou, J. R., Huang, J. X., The application of Geostatistics in Mineral Reserves Calculation, Earthquake Press, 1990.
17. Sun, H. Q., Geostatistics and Application. Beijing: China Mine University Press, 1990.
18. Eulogio, P. I., Peter, A. D., A computer program for estimating the semi-variogram and its uncertainty, Computers and Geosciences, No.275, 2001, pp. 549-561.
19. Matheron, G. M., Principles of geostatistics, Economic Geology, Vol. 58, No. 8, 1963, pp. 1246-1266.
20. Eulogio, P. I., Peter, A. D., VARIOG2D: a computer program for estimating the semi-variogram and its uncertainty, Computers and Geosciences, Vol.27, No.5, 2001, pp. 549-561.
21. Woodcock, C., Strahler, A., Jupp, D., The use of variograms in remote sensing: I..Scene models and simulated images, Remote Sensing of Environment, Vol. 25, No. 3, 1988, pp. 323-348.
22. Dell' Acqua, F., Gamba, R., Trianni, G., Semi-automatic choice of scale-dependent features for satellite SAR image classification, Pattern Recognition Letters, Vol. 27, 2006, pp. 244-251.
23. Timothy, C., Haas, Kriging and automated variogram modeling within a moving window, Atmospheric Environment, Vol.24, No.7, 1990, pp. 1759-1769.
24. Puig, D., Garcia, M. A., Determining Optimal Window Size for Texture Feature Extraction Methods, IX Spanish Symposium on Pattern Recognition and Image Analysis, Vol.2, 2001, pp. 237-242.
25. Ferro, C. J., Warner, T.A., Scale and texture in digital image classification, Photogrammetric Engineering and Remote Sensing, Vol.68, No.1, 2002, pp. 51-63.
26. Park, M., Jesse, J. S., Wilson, L. S., Hierarchical Indexing Images Using Weighted Low Dimensional Texture Features, IAPR Vision Interface, Calgary, 2002, pp. 39-44.
27. Zhang, J. G., Tan, T. N., Brief review of invariant texture analysis methods, Pattern Recognition, Vol. 35, No.3, 2002, pp.735-747.
28. Pietikainen, M., Ojala, T., Xu, Z., Rotation-Invariant texture classification using feature distributions, Pattern Recognition, Vol 33, No. 1, 2000, pp. 43-52.