Publication Type:

Journal Article

Source:

Advances in Intelligent Systems and Computing, Springer Verlag, Volume 380, IC3T 2015; Hyderabad; India, p.365-373 (2016)

ISBN:

9788132225225

URL:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-84945897035&partnerID=40&md5=c25f194b86bc41d26ee51445ccc09752

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. © Springer India 2016.

Cite this Research Publication

N. Nechikkat, Sowmya V., and Soman, K. P., “Variational mode feature-based hyperspectral image classification”, Advances in Intelligent Systems and Computing, vol. 380, pp. 365-373, 2016.

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