Publication Type : Journal Article, Conference Paper
Publisher : Communications in Computer and Information Science
Source : Communications in Computer and Information Science, Springer Verlag, Volume 968, p.256-267 (2019)
Url : https://www.scopus.com/inward/record.uri?eid=2-s2.0-85059944107&doi=10.1007%2f978-981-13-5758-9_22&partnerID=40&md5=28839f566e841563dc803b41fb398dd7
ISBN : 9789811357572
Keywords : Classification (of information), Classification accuracy, Deep learning, Dimensionality reduction, Dynamic mode decompositions, GURLS, Hyperspectral imaging, image classification, LIBSVM, Spectroscopy, Support vector machines
Campus : Coimbatore
School : School of Engineering
Center : Computational Engineering and Networking
Department : Electronics and Communication
Verified : No
Year : 2019
Abstract : Hyperspectral images are remotely sensed high dimension images, which capture a scene at different spectral wavelengths. There is a high correlation between the bands of these images. For an efficient classification and processing, the high data volume of the images need to be reduced. This paper analyzes the effect of dimensionality reduction on hyperspectral image classification using vectorized convolution neural network (VCNN), Grand Unified Recursive Least Squares (GURLS) and Support Vector Machines (SVM). Inorder to analyze the effect of dimensionality reduction, the network is trained with dimensionally reduced hyperspectral data for VCNN, GURLS and SVM. The experimental results shows that, one-sixth of the total number of available bands are the maximum possible reduction in feature dimension for Salinas-A and one-third of the total available bands are for Indianpines dataset that results in comparable classification accuracy. © 2019, Springer Nature Singapore Pte Ltd.
Cite this Research Publication : K. S. Charmisha, Sowmya, and Dr. Soman K. P., “Dimensionality reduction by dynamic mode decomposition for hyperspectral image classification using deep learning and kernel methods”, in Communications in Computer and Information Science, 2019, vol. 968, pp. 256-267.