Ant Colony Optimized AmoebaNet-A Algorithm for Hyperspectral Image Classification

Abstract

Hyperspectral imaging is one of the most widely used imaging techniques in numerous real-time applications. The detailed spectral information provided by hyperspectral imaging (HSI) is one of its main advantages. Each pixel has spectral information, and it can be effectively analyzed from hyperspectral images.The relationship among the high-resolution and object groups is carefully incorporated into the classification.Classifying hyperspectral images through conventional classification techniques is quite complex. Recently, deep learning techniques and their substantial potential in feature extraction have been proven in numerous research studies. Various non-linear problems are effectively solved through deep learning techniques. Conventional deep learning models based HSI classification approaches lags in performance, Thus, an efficient deep learning model, AmoebaNet-A, is presented in this research work for HSI classification. Additionally, nature inspired ant colony model is incorporated for network parameter optimization. Simulation analysis of the presented approach validates the improved performance using two data sets like the Indian Pines (IP) dataset and Italy's University of Pavia dataset (UP). Comparative analysis with existing approaches like optimized Self-organized map, EN-B4-SRO validates the higher performances of proposed model using the metrics like average accuracy, kappa coefficient and overall accuracy.