Dimensionality Reduction by Dynamic Mode Decomposition for Hyperspectral Image Classification Using Deep Learning and Kernel Methods
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.
KeywordsGURLS SVM Hyperspectral image classification Dimensionality reduction Dynamic mode decomposition Libsvm Gurls Classification accuracies
- 3.Dixon, K.D.M., Sowmya, V., Soman, K.P.: Effect of denoising on vectorized convolutional neural network for hyperspectral image classification. In: Nandi, A.K., Sujatha, N., Menaka, R., Alex, J.S.R. (eds.) Computational Signal Processing and Analysis. LNEE, vol. 490, pp. 305–313. Springer, Singapore (2018). https://doi.org/10.1007/978-981-10-8354-9_28CrossRefGoogle Scholar
- 6.Megha.P, Sowmya V., Soman, K.P.: Effect of dynamic mode decomposition based dimension reduction technique on hyperspectral image classification. In: International Conference on Nextgen Electronic Technologies: Silicon to Software (ICNETS2). LNEE Springer Proceedings, VIT University, Chennai Campus, India, pp. 23–25 (2017)Google Scholar