A Combined Detail Enhancing Algorithm and Texture Feature Extraction Method for Supervised Classification of Remote Sensing Images
- 165 Downloads
In this paper, we propose a supervised classification in multispectral satellite images based on a novel detail enhancing texture feature extraction algorithm. The multispectral training and test images are first given for pre-processing, which is decomposed into low-pass approximation and high-pass multi-directional subbands by wavelet based contourlet transform. High pass subbands are easily interfered by noise. Based upon the Normal Shrink technique, thresholding is applied in high frequency images to eliminate the noise. The intra and inter scale fusion rule is used to combine the approximation and detail subbands to form the enhanced image. The co-occurrence features are calculated by forming the gray level co-occurrence matrix on training and test images. Mahalanobis distance classifier is applied on the training and test data sets for effective classification. The experiment result shows that the overall accuracy is improved to 2.2% for (Test-1) 2% for (Test-2) and 3.2 5% for (Test-3) and kappa coefficient is improved to 0.02 for (Test-1) image 0.03 for (Test-2) image and 0.03 (Test-3) image.
KeywordsWavelet based contourlet transform Thresholding Intra and inter scale fusion Gray level co-occurrence matrix Mahalanobis distance classifier
- Kaur, M., Sharma, K., & Dhillon, N. (2013). Image denoising using wavelet thresholding. International Journal of Engineering and Computer Science, 2(10), 2932–2935.Google Scholar
- Li, D., Liu, J., Zhou, Q., Wang, L. & Huang, Q. (2011). Study on information extraction of rape acreage based on TM remote sensing image. In IEEE conference on geoscience and remote sensing (pp. 3323–3326).Google Scholar
- Lillesand, M. T., Ralph Kiefer, W., & Jonathan Chipman, W. (2004). Remote sensing and image interpretation (5th Wiley International ed., pp. 586–592).Google Scholar
- Prakash, O., Srivastava, R., & Khare, A. (2013). Biorthogonal wavelet transform based image fusion using absolute maximum fusion rule. In IEEE conference on information and communication technologies (pp. 577–582).Google Scholar