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Hybrid Model with Super Resolution and Decision Boundary Feature Extraction and Rule based Classification of High Resolution Data
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 101 - Number 8 |
| Year of Publication: 2014 |
| Authors: Navjeet Kaur, Simarjeet Kaur |
10.5120/17704-8700
|
Navjeet Kaur, Simarjeet Kaur . Hybrid Model with Super Resolution and Decision Boundary Feature Extraction and Rule based Classification of High Resolution Data. International Journal of Computer Applications. 101, 8 ( September 2014), 1-6. DOI=10.5120/17704-8700
Abstract
Data Mining is defined as the process of applying specific algorithms for extracting patterns from data. Data mining strategies can be grouped into Classification, Prediction, Estimation, Association Rule Mining and Clustering. Satellite images are used these days. With the help of high resolution image we can extract the features of an image. There are many types of satellites which provide accurate and high resolution images to individuals and organizations. High resolution images give detail information about rural, land, buildings, streets etc. Applications of High resolution images are defense, disaster management and medicals. Object identification is to identify an object which belongs to and feature extraction to extract features from the image like size, shape, texture and spectral information of the image. With the help of feature extraction techniques we can easily find the relevant information from the data and the data can easily be classified. In this hybrid error super resolution model and decision boundary feature extraction algorithms are used and then both the algorithms are compared. This gives the better accuracy with the help of kappa coefficient.
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