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HCGAN-Net: Classification of HSIs using Super PCA based Gabor Filtering with GAN

The hyperspectral image (HSI) classification applications become widespread in multiple applications. Therefore, the accurate classification of ground features using HSIs is an important research topic that has received a lot of interest. However, the conventional approaches failed to classify the pixels perfectly, which resulted in reduced accuracy. Thus, this work is focused on implementation of HSI classification using generative adversarial networks (HCGAN-Net). Initially, Gabor filtering is applied on HSIs for elimination of different types of noises, which also extracts the spatial features. Then, probabilistic principal component analysis (PCA) is applied to perform the dimensionality reduction operation, which also selects the best features using probability dependencies between spatial features. Then, spatial features are extracted for each spectral band and forms the hybrid spatial-spectral features. Finally, HCGAN-Net is used to performs the HSI classification operation using trained spatial-spectral features. The simulation results show that the proposed HCGAN-Net resulted in superior subjective and objective performance as compared to state of art HSI classifiers on various datasets.