Publication Type : Conference Paper
Publisher : Springer Verlag
Source : Lecture Notes in Electrical Engineering, Springer Verlag, Volume 500, p.171-179 (2019)
Url : https://www.scopus.com/inward/record.uri?eid=2-s2.0-85053634246&doi=10.1007%2f978-981-13-0212-1_18&partnerID=40&md5=13d5fdc622475f6fd267dcb85d1d1479
ISBN : 9789811302114
Keywords : Classification (of information), Convolution, Convolution neural network, Deep learning, Dimensionality reduction, Dynamic mode decompositions, Dynamics, eigenvalues and eigenfunctions, Hyperspectral imaging, image classification, Independent component analysis, Learning algorithms, Learning rates, Spectral resolution, Spectroscopy, Trainable parameters
Campus : Coimbatore
School : School of Engineering
Center : Computational Engineering and Networking
Department : Electronics and Communication
Verified : No
Year : 2019
Abstract : Hyperspectral images (HSIs) cover a wide range of spectral bands in the electromagnetic spectrum with a very finite interval, and with high spectral resolution of data. The main challenges encountered with HSIs are those associated with their large dimensions. To overcome these challenges we need a healthy classification technique, and we need to be able to extract required features. This chapter analyzes the effect of dimensionality reduction on vectorized convolution neural networks (VCNNs) for HSI classification. A VCNN is a recently introduced deep-learning architecture for HSI classification. To analyze the effect of dimensionality reduction (DR) on VCNN, the network is trained with dimensionally reduced hyperspectral data. The network is tuned in accordance with the learning rate and number of iterations. The effect of a VCNN is analyzed by computing overall accuracy, classification accuracy, and the total number of trainable parameters required before and after DR. The reduction technique used is dynamic mode decomposition (DMD), which is capable of selecting most informative bands using the concept of eigenvalues. Through this DR technique for HSI classification using a VCNN, comparable classification accuracy is obtained using the reduced feature dimension and a lesser number of VCNN trainable parameters. © Springer Nature Singapore Pte Ltd. 2019.
Cite this Research Publication : K. S. Charmisha, Sowmya, and Dr. Soman K. P., “Dimensionally reduced features for hyperspectral image classification using deep learning”, in Lecture Notes in Electrical Engineering, 2019, vol. 500, pp. 171-179.