Feature extraction and classification of hyperspectral imaging using minimum noise fraction and deep convolutional neural network

Abstract

Remote sensing technology is constantly developing, which has greatly expanded the use of hyperspectral imaging (HSI). In the arena of remote sensing, the classification of HSI has become a challenging topic. The unique properties of hyperspectral data make accurate categorization difficult. In recent years, deep structured learning has emerged as an effective feature extraction technique for effectively addressing nonlinear problems. It is now extensively used to solve a variety of image processing problems. Deep learning is used to classify images and has shown good performance in recent years. The exact categorization of ground topographies from hyperspectral data is a crucial and current research topic that has gotten a lot of attention. This research work focuses on HSI categorization utilizing several machine learning approaches, such as support vector machine, K-nearest neighbor, and convolutional neural network (CNN). To reduce the number of superfluous and noisy bands in the dataset, principal component analysis and minimum noise fraction (MNF) were utilized. Different performance evaluation measures, such as time taken for testing, classification accuracy, kappa accuracy, precision, recall, specificity, F1_score, and G-mean, were taken to prove the efficacy of the models. The simulation results show that the combination of MNF and CNN produces better classification accuracy compared with the other considered models.