Gene

Quantum mechanics

Gaussian

Support vector machine

Robustness (evolution)

Contextual image classification

Biochemistry

Polynomial kernel

Computer science

Polynomial

Radial basis function kernel

Physics

Kernel (algebra)

Pattern recognition (psychology)

Mathematics

Image (mathematics)

Kernel method

Hyperspectral imaging

Mathematical analysis

Artificial intelligence

Combinatorics

Gaussian function

Chemistry

Deep learning

Artificial neural network

Machine learning

Radial basis function

Pattern Recognition

To improve on the robustness of traditional machine learning approaches, emphasis has recently shifted to the integration of such methods with Deep Learning techniques. However, the classification problems, complexity and inconsistency in several spectral classifiers developed for hyperspectral images are some reasons warranting further research. This study investigates the application of Deep Support Vector Machine (DSVM) for hyperspectral image classification. Two hyperspectral images, Indian Pines and University of Pavia are used as tentative test beds for the experiment. The DSVM is implemented with four kernel functions: Exponential Radial Basis Function (ERBF), Gaussian Radial Basis Function (GRBF), neural and polynomial. Stand-alone Support Vector Machines form the interconnecting weights of the entire network. The network is trained with one hundred input datasets, and the interconnecting weights of the network are initialised using the regularisation parameter of the model. Numerical results show that the classification accuracies of the DSVM for Indian Pines and University of Pavia based on each DSVM kernel functions are: ERBF (98.87%, 98.16%), GRBF (98.90%, 98.47%), neural (98.41%, 97.27%), and polynomial (99.24%, 98.79%). By comparing the DSVM algorithm against well-known classifiers, Support Vector Machine (SVM), Deep Neural Network (DNN), Gaussian Mixture Model (GMM), K Nearest Neighbour (KNN), and K Means (KM) classifiers, the mean classification accuracies for Indian Pines and University of Pavia are: DSVM (98.86%, 98.17%), SVM (76.03%, 73.52%), DNN (94.45%, 93.79%), GMM (76.82%, 78.35%), KNN (76.87%, 78.80%), and KM (21.65%, 18.18%). These results indicate that the DSVM outperformed the other classification algorithms. The high accuracy obtained with the DSVM validates its efficacy as state-of-the-art algorithm for hyperspectral image classification.

Deep support vector machine for hyperspectral image classification