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Abstract

The old-fashioned hard classification techniques are parametric in nature and they expect data to follow a Gaussian distribution, they have been found to be performing poorly on high resolution satellite images. The classes in these images tend to exhibit wide-ranging coinciding in spectral space. Digital image classification is the process of sorting all the pixels in an image into a finite number of individual classes. Decision making was performed in two stages: feature extraction using the Wavelet Packet Transforms (WPT) and the ANFIS trained with the back propagation gradient descent method in combination with the least squares method for classification .Decision tree algorithm based approach is analysed for the selection of a subset from the combination of Wavelet Packet Spatial Features and Wavelet Packet Co-occurrence (WPC) textural feature set, which are used to classify the multispectral images. Overall accuracy, sensitivity, specification are used to assess the accuracy of the classified data.

References

1. S. Rajesh & S. Arivazhagan & K. Pratheep Moses & R. Abisekaraj (2013),“ANFIS Based Land Cover/Land Use Mapping of LISS IV Imagery Using Optimized Wavelet Packet Features”, Indian Society of Remote Sensing.

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 multi texture 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. Kent, J. T., & Mardia, K. V. (1986). “Spectral classification fuzzy membership models”. IEEE Transactions Pattern Analysis and Machine Intelligence, 10(4), 659–671.

6. Lillesand, M. T., Ralph Kiefer, W. & Jonathan Chipman, W. (2004). “Remote sensing and image interpretation”, 5th edn. Wiley International edition, pp. 586–592.

7. 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.

8. Rajesh, S., & Arivazhagan, S. (2011) “Land cover/land use mapping using different wavelet packet transforms for LISSIV imagery” .Proceedings of the IEEE International conference on computer ,communication &l electrical Technology (ICCCET 2011) pp.103–108.

9. 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.

10. 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.

Keywords

Wavelet packet transforms, Wavelet packet spatial, Wavelet packet co-occurance, Decision tree, adaptive neuro fuzzy inference system.