Publication Type : Conference Proceedings
Publisher : Advances in Intelligent Systems and Computing, Second International Conference on Computer and Communication Technologies (IC3T-2015)
Source : Advances in Intelligent Systems and Computing, Second International Conference on Computer and Communication Technologies (IC3T-2015), Springer, Volume 380, CMR Technical Campus, Hyderabad , p.365-373 (2016)
Url : http://link.springer.com/chapter/10.1007/978-81-322-2523-2_35
Keywords : classification, Hyperspectral imaging, Orthogonal matching pursuit, Variational mode decomposition
Campus : Coimbatore
School : School of Engineering
Center : Computational Engineering and Networking
Department : Electronics and Communication
Verified : Yes
Year : 2016
Abstract : Hyperspectral image analysis is considered as a promising technology in the field of remote sensing over the past decade. There are various processing and analysis techniques developed that interpret and extract the maximum information from high-dimensional hyperspectral datasets. The processing techniques significantly improve the performance of standard algorithms. This paper uses variational mode decomposition (VMD) as the processing algorithm for hyperspectral data scenarios followed by classification based on sparse representation. Variational Mode Decomposition decomposes the experimental data set into few different modes of separate spectral bands, which are unknown. These modes are given as raw input to the classifier for performance analysis. Orthogonal matching pursuit (OMP), the sparsity-based algorithm is used for classification. The proposed work is experimented on the standard dataset, namely Indian pines collected by the airborne visible/infrared imaging spectrometer (AVIRIS). The classification accuracy obtained on the hyperspectral data before and after applying Variational Mode Decomposition was analyzed. The experimental result shows that the proposed work leads to an improvement in the overall accuracy from 84.82 to 89.78 %, average accuracy from 85.03 to 89.53 % while using 40 % data pixels for training.
Cite this Research Publication : N. Nechikkat, Sowmya, and Dr. Soman K. P., “Variational Mode Feature-Based Hyperspectral Image Classification”, Advances in Intelligent Systems and Computing, Second International Conference on Computer and Communication Technologies (IC3T-2015), vol. 380. Springer, CMR Technical Campus, Hyderabad , pp. 365-373, 2016.