A novel hybrid optimization model-based deep CNN model for hyperspectral image classification

Recently, there have been continuous advancements in hyperspectral image classification (HSIC) approaches based on deep learning mechanisms. Nonetheless, information redundancy, high complexity and ineffective descriptions remain primary obstacles to present HSIC networks. In order to overcome these issues, an optimization-enabled deep convolutional neural network (Deep CNN) for HSIC is proposed in this work. Here, band selection, feature extraction, as well as classification are important tasks in processing these images. The band selection is performed by utilizing the double exponential smoothing-artificial flora optimization (DES-AFO) algorithm, which is the combination of double exponential smoothing (DES) and artificial flora optimization (AFO) for HSIC. There is a requirement for extracting competent features from hyperspectral imaging (HSI) to enhance the classifier’s accuracy. Here, the feature extraction is done by empirical wavelet transform and a convolutional neural network (CNN). As the next process, HSIC takes place using the Deep CNN, and it is trained by DES-AFO. The wide experimental outcomes on HSI show that the proposed mechanism has noteworthy benefits estimated with the conventional mechanisms concerning accuracy and TPR. Here, analysis states that the proposed mechanism obtained better accuracy and TPR values of 0.930, as well as 0.965.