An adaptive method with integration of multi-wavelet based features for unsupervised classification of SAR images

Journal of Geophysics and Engineering

Volumn 4, Issue 4, 2007, Pages 384-393

  Chamundeeswari, V V ^a   Singh, D ^a   Singh, K ^a  

^a INDIAN INSTITUTE OF TECHNOLOGY ROORKEE   (India)

Author keywords

M band wavelet packets; Neuro fuzzy feature evaluation; SAR images; Texture segmentation; Unsupervised classification

Indexed keywords

ACCURACY ASSESSMENT; ALGORITHM; FUZZY MATHEMATICS; IMAGE; LAND COVER; SYNTHETIC APERTURE RADAR; WAVELET;

EID: 38849116434     PISSN: 17422132     EISSN: 17422140     Source Type: Journal    
DOI: 10.1088/1742-2132/4/4/004     Document Type: Article

References (19)

1. Acharyya M, Rajat K De and Kundu M 2003 Segmentation of remotely sensed images using wavelet features and their evaluation in soft computing framework IEEE Trans. Geosci. Remote Sens. 41 2900-5
2. Alkin O and Caglar H 1995 Design of efficient M-band coders with linear phase and perfect reconstruction properties IEEE Trans. Signal Process. 43 1579-90
3. Chang T and Jay Kuo C C 1993 Texture analysis and classification with tree structured wavelet transform IEEE Trans. Image Process. 2 429-41
4. Chamundeeswari V V and Singh D 2006 Computationally efficient extraction and integration of multi wavelet based features for segmentation of SAR images Proc. ISPRS TC-IV and ISRS Symp. on Geospatial Databases for Sustainable Development pp 69-74
5. Congalton B G 1983 A quantitative method to test for consistency and correctness in photointerpretaion Photogramm. Eng. Remote Sens. 49 69-74
6. Dekker Rob J 2003 Texture analysis and classification of ERS SAR images for map updating of urban areas in the Netherlands IEEE Trans. Geosci. Remote Sens. 41 1950-8
7. Haralick R M, Shanmugam K and Dinstein I 1973 Texture feature for image classification IEEE Trans. Syst. Man Cybern. 8 610-21
8. Huang K and Aviyente S 2006 Information-theoretic wavelet packet sub-band selection for texture classification Signal Process. 86 1410-20
9. Jain A K and Farrokhnia F 1991 Unsupervised texture segmentation using Gabor filters Pattern Recognit. 24 1167-86
10. Jayant N S and Noll P 1984 Digital Coding of Waveforms: Principles and Applications to Speech and Video (Englewood Cliffs, NJ: Prentice-Hall)
11. Lee J S 1980 Digital image enhancement and noise filtering by use of local statistics IEEE Trans. Pattern Anal. Mach. Intell. 42 165-8
12. Leporini D and Pesquet J C 1997 High order properties of M-band wavelet packet decompositions Proc. 1997 IEEE Signal Processing Workshop on Higher-Order Statistics p 430
13. Mallat S 1989 A theory for multi-resolution signal decomposition: the wavelet representation IEEE Trans. Pattern Anal. Mach. Intell. 11 674-93
14. Manjunath B S and Chellappa R 1991 A note on unsupervised texture segmentation IEEE Trans. Pattern Anal. Mach. Intell. 13 478-83
15. Pal S K, De R K and Basak J 2000 Unsupervised feature evaluation: a neuro-fuzzy approach IEEE Trans. Neural Netw. 11 366-76
16. Steffen P, Heller P N, Gopinath R A and Burrus C S 1993 Theory of regular M-band wavelet bases IEEE Trans. Signal Process. 41 3497-510
17. Ulaby F T, Allen C T, Eger G and Kanemasu E 1984 Relating the microwave backscattering coefficient to leaf area index Remote Sens. Environ. 14 113-33
18. Unser M 1995 Texture classification and segmentation using wavelet frames IEEE Trans. Image Process. 4 1549-60
19. Zou H and Tewfik A H 1992 Discrete orthogonal, M-band wavelet decomposition Proc. Int. Conf. Acoustics, Speech, and Signal Processing vol 4 pp IV-605-IV-608