Deep proximal support vector machine classifiers for hyperspectral images classification

In this work, an effective classification of hyperspectral images is modelled and simulated with the proximal support vector machine (PSVM) by integrating them with the deep learning approach. The modelled new deep proximal support vector machines are designed in a manner to handle the existing complexity, discrepancies and irregularities in the traditional hyperspectral image classifiers. This paper investigates the applicability of the new deep linear and nonlinear proximal support vector machines as applied for hyperspectral image classification. In respect of the new deep PSVM classifier, it is modelled for deep linear PSVM and deep nonlinear PSVM to perform classification of spectral images so as to bring out the best classifier model. To test and validate the proposed deep PSVM classifiers University of Pavia datasets, Indian Pine datasets and Kennedy Space Centre datasets are employed as test beds and results are attained. The developed new deep PSVM classifiers are developed with varied kernel functions to do the classification process. The deep learning technique enhances the linear and nonlinear PSVM classifier models to perform more effectively during the learning process and carry out the classification using auto-encoders and decoders. Results attained during the process infer that the developed new deep PSVM (linear and nonlinear) has come out with better classification accuracy in comparison with that of the other techniques from literature for the same datasets. Statistical analysis validates the randomness that occurs in the proposed deep learning techniques as applied for spectral image classification.