ANFIS Based Land Cover/Land Use Mapping of LISS IV Imagery Using Optimized Wavelet Packet Features

Journal of the Indian Society of Remote Sensing

Volumn 42, Issue 2, 2014, Pages 267-277

  Rajesh, S ^a   Arivazhagan, S ^a   Moses, K Pratheep ^b   Abisekaraj, R ^c  

^a MEPCO SCHLENK ENGINEERING COLLEGE   (India)

^b ANNA UNIVERSITY   (India)

^c Defence Research Development Organization   (India)

Author keywords

Adaptive neuro fuzzy inference system; Genetic algorithms; Mixed pixels classification; Normalized vegetation index; Wavelet co occurrence feature; Wavelet packet decomposition; Wavelet statistical feature

Indexed keywords

DIGITAL IMAGE; GENETIC ALGORITHM; IMAGE CLASSIFICATION; LAND COVER; LAND USE CHANGE; MAPPING METHOD; PIXEL; REMOTE SENSING;

EID: 84901609512     PISSN: 0255660X     EISSN: 09743006     Source Type: Journal    
DOI: 10.1007/s12524-013-0276-1     Document Type: Article

References (33)

1. Acharyya, M, & Kundu, M. K. (2001). Wavelet-based texture segmentation of remotely sensed images. In Proc. International Conference on Image Analysis and Processing, pp. 69-74.
2. Acharyya, M., & Kundu, M. K. (2007). Image segmentation using wavelet packet frames and neuro-fuzzy tools. International Journal of Computational Cognition, 5(4), 27-43.
3. Acharyya, M., De, R. K., & Kundu, M. K. (2003a). Extraction of features using M-band wavelet packet frame and their neuro-fuzzy feature evaluation for multitexture segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(12), 1639-1644.
4. Acharyya, M., De, R. K., & Kundu, M. K. (2003b). Segmentation of remotely sensed images using wavelet features and their evaluation in soft computing framework. IEEE Transactions on Geoscience and Remote Sensing, 41(12), 2900-2905.
5. Arivazhagan, S., & Ganesan, L. (2003). Texture classification using wavelet transform. Pattern Recognition Letters, 24, 1513-1521.
6. Arivazhagan, S., & Ganesan, L. (2005) Performance analysis of texture classification techniques using MRMRF and WSFS & WCFS. Proceedings of the Sixth International conference on computational Intelligence and Multimedia Applications (ICCIMA'05), pp. 297-302, doi: 10. 1109/ICCIMA. 2005. 46.
7. Baraldi, A., & Parmiggiani, F. (1995). An investigations of the textural characteristics associated with gray level co-occurrence matrix statistical parameters. IEEE Transaction on Geoscience and Remote Sensing, 33(2), 293-304.
8. Chen, C., & Chen, D. C. (1996). Multiresolution gabor filters in texture analysis. Pattern Recognition Letters, 17(10), 1069-1076.
9. Congalton, R. G. (1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37, 35-46.
10. Fukuda, S., & Hirosawa, H. (1999). A wavelet-based texture feature set applied to classification of multifrequency polarimetric SAR images. IEEE Transactions on Geoscience and Remote Sensing, 7(5), 2282-2286.
11. Fung, T. (1990). An assessment of TM imagery for land-cover change detection. IEEE Transactions on Geoscience and Remote Sensing, 28(4), 681-684.
12. Gogoi, R., & Sarma, K. K. (2012). ANFIS based information extraction using K-means clustering for application in satellite images. International Journal of Computer Applications, 50(7), 13-18.
13. Haralick, R. M., Shanmugam, K., & Dinstein, I. (1973). Textural features for image classification. IEEE Transactions on Systems, Man, and Cybemetics, 3(6), 610-621.
14. Hsu, P.-H., Tseng, Y. H., & Gong, P. (2006). Spectral feature extraction for hyper spectral images using wavelet transform. Journal of Photogrammetry and Remote Sensing, 11(1), 93-109.
15. Huang, X., Zhang, L., & Li, P. (2008). A multi scale feature fusion approach for classification of very high resolution satellite imagery based on wavelet transform. International Journal of Remote Sensing, 29(20), 5923-5941.
16. Jang, J. S. R. (1993). ANFIS: adaptive-network-based fuzzy inference systems. IEEE Transactions on Systems, Man, and Cybernetics, 23, 665-685.
17. Joshi, & Chandra, P. (2011). Performance evaluation of vegetation indices using remotely sensed data. International Journal of Geomatics and Geosciences, 2(1), 231-240.
18. Kent, J. T., & Mardia, K. V. (1986). Spectral classification fuzzy membership models. IEEE Transactions Pattern Analysis and Machine Intelligence, 10(4), 659-671.
19. Lillesand, M. T., Ralph Kiefer, W. & Jonathan Chipman, W. (2004). Remote sensing and image interpretation, 5th edn. Wiley International edition, pp. 586-592.
20. Lindsay, R. W., Percival, D. B., & Rothrock, D. A. (1996). The discrete wavelet transform and the scale analysis of the surface properties of sea ice. IEEE Transactions on Geoscience and Remote Sensing, 34(3), 771-787.
21. Mallat, S. G. (1989). A theory for multiresolution signal decomposition: the wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(7), 674-693.
22. Murtagh, F., & Starck, J. L. (2008). Wavelet and curvelet moments for image classification: application to aggregate mixture grading. Pattern Recognition Letters, 29(10), 1557-1564.
23. Pal, M. (2006). Support vector machine-based feature selection for land cover classification: a case study with DAIS hyper spectral data. International Journal of Remote Sensing, 27, 2877-2894.
24. Raja, S. A., Anand, V., Maithani, S., & Senthil Kumar, A. (2009). Wavelet Frame based feature extraction technique for improving classification accuracy. Journal of Indian Society of Remote Sensing, 37, 423-431.
25. Rajesh, S., & Arivazhagan, S. (2011) Land cover/land use mapping using different wavelet packet transforms for LISS IV imagery. Proceedings of the IEEE International conference on computer, communication & l electrical Technology (ICCCET 2011) pp. 103-108.
26. Rajesh, S., Arivazhagan, S., Pradeep Moses, K., & Abisekaraj, R. (2012a). Land cover/land use mapping using different wavelet packet transforms for LISS IV Madurai imagery. Journal of the Indian Society of Remote Sensing, 40(2), 313-324.
27. Rajesh, S., Arivazhagan, S., Pradeep Moses, K., Abisekaraj, R. (2012) Genetic algorithm based feature subset selection for land cover/land use mapping using wavelet packet transform. Journal of the Indian Society of Remote Sensing, (in Press) doi: 10. 1007/s12524-012-0208-5.
28. Saradhadevi, V., & Sundaram, V. (2012). An enhanced two-stage impulse noise removal technique for SAR images based on fast ANFIS and fuzzy decision. European Journal of Scientific Research, 68(4), 506-522.
29. Shankar, B. U., Meher, S. K., & Ghosh, A. (2011). Wavelet-fuzzy hybridization: feature-extraction and land-cover classification of remote sensing images. Applied Soft Computing, 11, 2999-3011.
30. Soman, K. P., & Ramachandran, K. I. (2005). Insight into wavelets - from theory to practice (2nd ed.). India: Prentice-Hall.
31. Thitimajshima, P. (1999) Multiresolution fuzzy clustering for SAR image segmentation. In Proc. of Geoscience and Remote Sensing Symposium, IGARSS'99, Vol. 5, pp. 2507-2509.
32. Unser, M. (1995). Texture classification and segmentation using wavelet frames. IEEE Transactions on Image Processing, 4(11), 1549-1560.
33. Zhang, Y., Backer, S. D., & Scheunders, P. (2009). Noise-resistant wavelet-based Bayesian fusion of multispectral and hyper spectral images. IEEE Transactions on Geo-Science and Remote Sensing, 47(11), 3834-3843.