Visible to the public Classification of Hyperspectral Images using Edge Preserving Filter and Nonlinear Support Vector Machine (SVM)

TitleClassification of Hyperspectral Images using Edge Preserving Filter and Nonlinear Support Vector Machine (SVM)
Publication TypeConference Paper
Year of Publication2020
AuthorsPerisetty, A., Bodempudi, S. T., Shaik, P. Rahaman, Kumar, B. L. N. Phaneendra
Conference Name2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS)
Keywordsdenoising, edge preserving filter, edge-preserving filter, factor analysis, factor analysis based dimensionality reduction technique, feature extraction, feature extraction phase, geophysical image processing, hyperspectral image, Hyperspectral imaging, image classification, learning (artificial intelligence), nonlinear support vector machine, nonlinear SVM, pubcrawl, Resiliency, Scalability, spatial, spatial filter, spatial information, spectral, spectral bands, support vector machine, Support vector machines, weight least square filter, work factor metrics
AbstractHyperspectral image is acquired with a special sensor in which the information is collected continuously. This sensor will provide abundant data from the scene captured. The high voluminous data in this image give rise to the extraction of materials and other valuable items in it. This paper proposes a methodology to extract rich information from the hyperspectral images. As the information collected in a contiguous manner, there is a need to extract spectral bands that are uncorrelated. A factor analysis based dimensionality reduction technique is employed to extract the spectral bands and a weight least square filter is used to get the spatial information from the data. Due to the preservation of edge property in the spatial filter, much information is extracted during the feature extraction phase. Finally, a nonlinear SVM is applied to assign a class label to the pixels in the image. The research work is tested on the standard dataset Indian Pines. The performance of the proposed method on this dataset is assessed through various accuracy measures. These accuracies are 96%, 92.6%, and 95.4%. over the other methods. This methodology can be applied to forestry applications to extract the various metrics in the real world.
Citation Keyperisetty_classification_2020