Automated Red Deer Algorithm with Deep Learning Enabled Hyperspectral Image Classification

Abstract

Hyperspectral (HS) image classification is a hot research area due to challenging issues such as existence of high dimensionality, restricted training data, etc. Precise recognition of features from the HS images is important for effective classification outcomes. Additionally, the recent advancements of deep learning (DL) models make it possible in several application areas. In addition, the performance of the DL models is mainly based on the hyperparameter setting which can be resolved by the design of metaheuristics. In this view, this article develops an automated red deer algorithm with deep learning enabled hyperspectral image (HSI) classification (RDADL-HIC) technique. The proposed RDADL-HIC technique aims to effectively determine the HSI images. In addition, the RDADL-HIC technique comprises a NASNetLarge model with Adagrad optimizer. Moreover, RDA with gated recurrent unit (GRU) approach is used for the identification and classification of HSIs. The design of Adagrad optimizer with RDA helps to optimally tune the hyperparameters of the NASNetLarge and GRU models respectively. The experimental results stated the supremacy of the RDADL-HIC model and the results are inspected interms of different measures. The comparison study of the RDADL-HIC model demonstrated the enhanced performance over its recent state of art approaches.