Intelligent Rider Optimization Algorithm with Deep Learning Enabled Hyperspectral Remote Sensing Imaging Classification

Abstract Hyperspectral imaging (HSI) can be attained by the use of high resolution optical sensors and it comprises several spectral bands of the identical remote sensing target and is treated as a three-dimension (3D) dataset. Recently, deep learning (DL) techniques are gained important attention among research communities for image classification. In this aspect, this study develops an intelligent rider optimization algorithm with deep learning enabled HSI classification model, named IRODL-HSIC technique. The proposed IRODL-HSIC technique aims to categorize the different class labels of the multispectral images. Besides, the IRODL-HSIC technique applies singular value decomposition. Moreover, the ResNet-152 technique was executed as a feature extractor to generate a collection of features. Furthermore, the rider optimization algorithm with cascaded recurrent neural network (CRNN) approach is utilized for the classification process. For ensuring the enhanced performance of the IRODL-HSIC algorithm, a wide range of simulations take place utilizing the multispectral images and the outcomes are examined under different aspects. The extensive comparative study highlighted the better performance of the IRODL-HSIC technique over the recent methods.