High Dimensionality is always a great concern while working with hyperspectral images. The high dimension of hyperspectral image increases the computational complexity, creates data storage issues and decrease the performance and accuracy of hyperspectral image analysis algorithms. This paper focuses on low dimensional Variational Mode features for hyperspectral image classification. The proposed method consist of three stages: preprocessing using Inter Band Block Correlation (IBBC) technique, feature extraction using Variational Mode Decomposition (VMD) and dimensionality reduction using Singular Value Decomposition (SVD). The efficiency of the proposed method based on the low dimensional feature extraction using VMD is evaluated by one of the sparsity based classification algorithms namely Orthogonal Matching Pursuit (OMP). The proposed work is experimented on the standard dataset namely Indian pines acquired by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). The experimental analysis shows that our proposed technique produces 90.88% overall accuracy with 40% of training which is greater than the classification accuracy obtained without feature extraction.
cited By 0
N. Nechikkat, Sowmya V., and Dr. Soman K. P., “Low dimensional variational mode features for hyperspectral image classification”, Journal of Chemical and Pharmaceutical Sciences, vol. 9, pp. 565-570, 2016.