XXII ISPRS Congress 2012: Technical Commission VII

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Persistent identifier:: 1663813779

Title:: XXII ISPRS Congress 2012

Sub title:: Melbourne, Australia, 25 August-1 September 2012

Year of publication:: 2013

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663813779

Language:: English

Additional Notes:: Kongress-Thema: Imaging a sustainable future

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Document type:: Multivolume work

Volume

Persistent identifier:: 1663821976

Title:: Technical Commission VII

Scope:: 546 Seiten

Year of publication:: 2013

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663821976

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(39,B7)

Language:: English

Additional Notes:: Erscheinungsdatum des Originals ist ermittelt.; Literaturangaben

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2019

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: [VII/6: REMOTE SENSING DATA FUSION]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: A TRANSFORMATION METHOD FOR TEXTURE FEATURE DESCRIPTION UNDER DIFFERENT IMAGINE CONDITIONS Z. Guan, J. Yu, T. Feng , A. Li

Document type:: Multivolume work

Structure type:: Chapter

5. SUMMARY In this paper, texture features description of multispectral imagery and SAR imagery are conducted with Gabor wavelet. Then we established the texture mapping between two registered images based on GMM and solved the parameters with EM algorithm. It can be seen from the results that the output of transformation has a high similarıty and expression with the SAR imagery texture. What’s more, the bigger the number of single Gaussians in one GMM is, the lower transformed error precision will be. But in fact, the amount of calculation will increase greatly since the number of single Gaussians in one GMM increasing. Hence, the value of m should be selected carefully in the experiment. Finally, the approach proposed in this paper of texture features transformed under different imaging conditions has been proved to be effective. ACKNOWLEDGEMENTS This research is supported by National Key Basic Research Program of China (973 Program, Grant No. 2012CB719903). At the same time, the project is supported by the National Natural Science Foundation of China (Grant No. 41171327). References Arivazhagan, S., Ganesan, L., Priyal, S. P., 2006. Texture classification using Gabor wavelets based rotation invariant features. Pattern Recognition Letters, 27 (16), pp. 1976-1982. Bovik, A., Clark, M., Geisler, W. S., 1990. Multichannel texture analysis using localized spatial filter. /EEE Transactions on Pattern Analysis and Machine Intelligence, 12(1), pp. 55-73. Clausi, D. A., Jernigan, M. E., 2000. Designing Gabor flters for optimal texture separability. Pattern Recgnition, 33(11), pp. 1835-1849. Chellappa, R., Chatterjee, S., 1985.Classification of texture using Gaussian Markov random fields. /EEE Transaction on Acoustics, Speech, and Signal Processing,33(4), pp. 959-963. Chitre, Y., Dhawan, A. P., 1999.M-band wavelet discrimination of natural textures. Pattern Recgnition, 32(5), pp. 773-789. Chen, Y., Wang, R., 2007. A method for texture classification by intergrating Gabor filters and ICA. Acta Electronica Sinica, 35(2), pp. 299-303. Dunn, D., Higgins W. E. Wakeley, J. 1994. Texture segmentation using 2-D Gabor elementary function. /EEE Transactions on Pattern Analysis and Machine Intelligence, 16(2), pp. 130-149. Duskunovic, L, Heene, G, Philips, W., Bruyland, L, 2000. Urban area detection in SAR imagery using a new speckle reduction technique and Markov random field texture classification. /nternational Geoscience and Remote Sensing Symposium. Honolulu, USA, July 24-28. Haralick, R. M., Shanmugam, K., Dinstein, L, 1973. Textural features for image classification. [EEE Transactions on Systems, Man, and Cybernetics, 3 (6), pp. 610-621 Hu, S, Guo D, Sheng, Y, 2001. Extracting textural information of satelite SAR image based on wavelet decomposition. Journal of Remote Sensing, 5(6), pp. 424-427. Ivanov, V. K., Paschenko, R. E., Stadnyk, O. M., etc, 2006. Radar remote sensing images segmentation using fractal dimension field. Radar Conference, pp. 217-220. Kain, A., Macon, M., 1998. Spectral voice conversion for text- to-speech synthesis. Proc ICASSP. Seattle, USA, May 12-15. Lopez-Espinoza, E. D., Altamirano-Robles, L., 2008. A method based on Tree-Structured Markov random field and a texture International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012 XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia energy function for classification of remote sensing images.Electrical Engineering, Computing Science and Automatic Control, CEE 2008, 5" , Mexico city, November 12-14. Lin, W., et al., 2007. A method of camouflage evaluation based on texture analysis model of Gabor wavelet. Acta Armamentarii, 28(10), pp. 1191-1194. Lin, W., et al., 2009. Detection probability evaluation model based on texture feature of thermal infrared. Infrared and Laster Engineering, 38(1), pp. 155-159. Li, Y., Meng, X., 2008. Image texture feature detection based on Gabor filter. Journal of Changchun University of Technology (Natural Science Edition), 29(1), pp. 78-81. Mallat, S. G, 1989. A theory for multiresolution signal decomposition: the wavelet reprentation. /EEE Transactions on Pattern Analysis and Machine Intelligence, 11(7), pp. 674-693. Ni, L., Zhang, J., Yao, W., 2004. SAR image’s texture analysis based on wavelet. Geomatics and Information Science of Wuhan University, 29(4), pp. 367-370. Pentland, A. P., 1984. Fractal-based description of natural scenes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6(6), pp. 661-674. Permuter, H., Francos, J., Jermyn, I., 2006. A study of Gaussian mixture models of color and texture features for image classification and segmentation. Pattern Recgnition,39(4), pp. 695-706. Soh, L., Tsatsoulis, C., 1999 Texture analysis of SAR sea ice imagery using gray level Co-occurrence matrices. IEEE Transactions on Geoscience and Remote Sensing, 37(2), pp. 780-795. Shu, N., 1998.Remote sensing image texture analysis and fractal assessment. Journal of Wuhan technical university of surveying and mapping, 23(4), pp. 370-373. Song, Y., Wan, Y., Chen, P., 2006. Textural features analysis based on GLCM in TM thermal infrared remotely sensed images . Remote Sensing Information, 4, pp. 24-26. Song, Y., Liu, B., Xie, J., 2010. Medical image texture features classification based on Gabor wavelet transform. Computer Engineering, 36(11), pp. 200-202. Tuceryan, M., Jain, A. K., 1993. Texture Analysis, Handbook Pattern Recognition and Computer Vision. World Scientific, Singapore, pp. 235-276. Wu, G, Zhang, Y., Lin, X., 2001. Optimal Gabor filter design for Bi-textured image segmentation. Acta Electronica Sinica, 29(1), pp. 48-50. Weszka, J. S., Dyer, C. R., Rosenfeld, A., 1976. A comparative study of texture measures for terrain classification. /EEE Transaction on Systems, Man, and Cybemetics, 6(4), pp. 269- 285.

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