A Comprehensive Study on Classification of Hyperspectral Imagery Using Machine Learning and Deep Learning Techniques

Abstract

One of the major challenges in automated road and building extraction in Indian earth space is the lack of information on highway projects and smart city development, specifically in urban and rural areas. It can be an excellent solution to upgrade road networks and buildings by using hyperspectral remote sensing images. Furthermore, the exact identification and retrieval of road and building footprints with high accuracy is possible when we use hyperspectral imagery. At the same time, the processing time will increase. The aerial photography and multispectral images quickly detect the streets, buildings, and other objects but the accuracy will be less when we compare with hyperspectral imagery. Because the hyperspectral have continuous spectral bands and standard clustered (spaced) spectral bands. To address these challenges,this paper presents a comparative analysis for automated building footprint extraction and road detection from hyperspectral images using machine learning and deep learning techniques. In this research to get high accuracy and reduced complexity machine learning and deep learning techniques are used in hyperspectral imagery. The FCN, SVM, and CNN classification techniques have yield better classification accuracy the process of automated building and road detection has been implemented with machine learning and deep learning methods yield improved accuracy.