Refine your search
Collections
Co-Authors
Year
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Ragul, K.
- An Ingenious Texture and Shape Feature Extraction in Remote Sensing Images by Means of Multi Kernel Principal Component analysis with Pyramidal Wavelet Transform and Canny Edge Detection Method
Abstract Views :181 |
PDF Views:3
Authors
N. Balakumar
1,
K. Ragul
1
Affiliations
1 Department of Computer Application, Pioneer College of Arts and Science, IN
1 Department of Computer Application, Pioneer College of Arts and Science, IN
Source
ICTACT Journal on Image and Video Processing, Vol 8, No 3 (2018), Pagination: 1681-1685Abstract
In the rapid growth of the digital world, the dealing of remote sensing image is increased day to day in context with the extraction of information. The feature extractions had been an exigent part among the research to classify the remote sensing images for legitimate information reclamation. In such context this paper focus on the extraction of information from remote sensing images by means of classification of spectral classes. Texture and shape is one of the important features in computer vision for many applications. Most of the attention has been focused on texture features with window selection and noise models. This problem can be overcome through Multi Kernel Principal Component analysis with pyramidal wavelet transform and canny edge detection method for extracting feature in high resolute images based on texture and shape. In this paper, proposed Multi Kernel Principal Component analysis utilizes to extract common information and specify common sets of features for further process and reduces dimensionality. Pyramidal wavelet transform is used to extract texture perception for visual interpretation and it decomposes the images into number of descriptors. So texture can be extracted in an image with tree-structured wavelet. Finally, an edge detection technique identifies the boundary regions from the classified remote sensing image, which is taken as shape feature extraction. The performance of this proposed work is measured through peak signal to noise ratio, Execution time, Kappa analysis and structural similarity for a various remote sensing dataset images.Keywords
Feature Extraction, Texture, Edge Detection, Remote Sensing Images.
References
- B. Sowmya and B. Sheelarani, “Land Cover Classification using Reformed Fuzzy C-Means”, Sadhana, Vol. 36, No. 2, pp. 153-165, 2011.
- Nora K. Speicher and Nico Pfeifer, “Towards Multipe Kernel Principal Component Analysis for Integrative Analysis of Tumor Samples”, Journal of Integrative Bioinformatics, Vol. 14, No. 2, pp. 1-6, 2017.
- Dhanalakshmi Renganathan, K.S. Riyas and Anu George, “Implementation of Canny Edge Detection Algorithm in FPGA using HDL”, International Journal of Innovative Research in Science, Engineering and Technology, Vol. 6, No. 5, pp. 21-27, 2017.
- R. Ponneela Vignesh and R. Rajendran, “Performance and Analysis of Edge Detection using FPGA Implementation”, International Journal of Modern Engineering Research, Vol. 2, No. 2, pp. 552-554, 2012.
- Wei Wang, Min Zhang, Dan Wang and Yu Jiang, “Kernel PCA Feature Extraction and the SVM Classification Algorithm for Multiple-Status, Through-Wall, Human being Detection”, EURASIP Journal on Wireless Communications and Networking, Vol. 151, pp. 112-117, 2017.
- Mahmoud Salah, “A Survey of Modern Classification Techniques in Remote Sensing for Improved Images”, Journal of Geomatics, Vol. 11 No. 1, pp. 1-21, 2017.
- Dao-Qiang Zhang, “A novel Kernelized fuzzy C-means Algorithm with Application in Medical Image Segmentation”, Artificial Intelligence in Medicine, Vol. 32, No. 1, pp. 37-50, 2004.
- Purva Singh, Kashish Arora, Shivin Sinha and R. Sathyaraj, “Survey on MR Image Segmentation using Fuzzy C-Means Algorithm”, International Journal of Engineering Research, Vol. 6, No. 5, pp. 261-264, 2017.
- Pablo Morales-Alvarez and Adrian Perez-Suay, “Remote Sensing Image Classification with Arge Scale Gaussian Processes”, IEEE Transaction on Geoscience and Remote Sensing, Vol. 56, No. 2, pp. 1103-1114, 2017.
- Muzhir Shaban Al-Ani and Alaa Sulaiman Al-Waisy, “Multi-View Detection Based on Kernel Principal Component Analysis and Kernel Support Vector Techniques”, International Journal of Soft Computing, Vol. 2, No. 2, pp. 1-13, 2011.
- Shengkun Xie, Anna T. Lawniczak, Sridhar Krishnan, and Pietro Lio, “Wavelet Kernel Principal Component Analysis in Noisy Multiscale Data Classification”, ISRN Computational Mathematics, Vol. 2012, pp. 1-13, 2012.
- Min Chen and Simone A. Ludwig, “Color Image Segmentation using Fuzzy C-Regression Model”, Hindawi Advances in Fuzzy Systems, Vol. 2017, pp. 1-15, 2017.
- R.V. Ramana, T.V. Rathnam and A. Sankar Reddy, “A Review on Edge Detection Algorithms in Digital Image Processing Applications”, International Journal on Recent and Innovation Trends in Computing and Communication, Vol. 5, No. 10, pp. 69-75, 2017.
- Bin Zhao, Lianru Gao, Wenzhi Liao and Bing Zhang, “A New Kernel Method for Hyperspectral Image Feature Extraction”, Geo-Spatial Information Science, Vol. 20, No. 3, pp. 309-318, 2017.
- Jian-Hua Song and Wang Cong and Jin Li, “A Fuzzy C-means Clustering Algorithm for Image Segmentation Using Nonlinear Weighted Local Information”, Journal of Information Hiding and Multimedia Signal Processing, Vol. 8, No. 9, pp. 1-11, 2017.
- Dixa Saxena, S.K. Saritha and K.N.S.S.V. Prasad, “Survey Paper on Feature Extraction Methods in Text Categorization”, International Journal of Computer Applications, Vol. 166, No. 11, pp. 1-7, 2017.
- Xin Zhang, Jintian Cui, Weisheng Wang and Chao Lin, “A Study for Texture Feature Extraction of High-Resolution Satellite Images based on a Direction Measure and Gray Level Co-Occurrence Matrix Fusion Algorithm”, Sensors, Vol. 17, No. 7, pp. 1474-1479, 2017.
- Giorgio Licciardi, Prashanth Reddy Marpu, Jocelyn Chanussot and Jon Atli Benediktsson, “Linear Versus Nonlinear PCA for the Classification of Hyper spectral Data based on the Extended Morphological Profiles”, IEEE Geosciences and Remote Sensing Letters, Vol. 9, No. 3, pp. 447-451, 2012.
- S.M. Isa, “Sleep Apnea Detection from ECG Signal: Analysis on Optimal Features, Principal Components, and Nonlinearity”, Proceedings of 5th International Conference on Bioinformatics and Biomedical Engineering, pp. 1-4, 2011.
- Feng Wang, Bo Cheng and Binggang Cao, “Machine Condition Monitoring by Nonlinear Feature Fusion based on Kernel Principal Component Analysis with Genetic Algorithm”, Proceedings of IEEE 3rd International Conference Natural Computation, pp. 665-670, 2007.
- Mohamed N. Ahmed et al., “Modified Fuzzy C-Means Algorithm for Bias Field Estimation and Segmentation of MRI Data”, IEEE Transactions on Medical Imaging, Vol. 21, No. 3, pp. 193-199, 2002.
- Ramanpreet Kaur and Sukhpreet Kaur, “Enhancement of Colored Images in Digital Image Processing”, International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 4, No. 7, pp. 489-492, 2014.
- Amiya Halder, Soumajit Pramanik and Arindam Kar, “Dynamic Image Segmentation using Fuzzy C-Means based Genetic Algorithm”, International Journal of Computer Applications, Vol. 28, No. 6, pp. 1-7, 2011
- Ada and Rajneet Kaur, “Feature Extraction and Principal Component Analysis for Lung Cancer Detection in CT Scan Images”, International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 3, No. 3, pp. 187-190, 2013.
- Soumi Ghosh and Sanjay Kumar Dubey, “Comparative Analysis of K-Means and Fuzzy C-Means Algorithms”, International Journal of Advanced Computer Science and Applications, Vol. 4, No. 4, pp. 35-39, 2014.