Publication Type : Journal Article
Publisher : Multimedia Tools and Applications
Source : Multimedia Tools and Applications, Springer New York LLC, Volume 78, Issue 13, p.18379-18394 (IF: 2.313, CiteScore: 3.7, Q1- 80 percentile). (2019)
Url : https://www.scopus.com/inward/record.uri?eid=2-s2.0-85060695028&doi=10.1007%2fs11042-019-7179-2&partnerID=40&md5=b889438695f94c8579745a181f188824
Keywords : Benchmarking, Classification (of information), Deep learning, image classification, Image processing, Infrared devices, land cover, Network architecture, Neural networks, Normalized difference vegetation index, SAT-4, SAT-6, Satellite image classification, Satellites, Trainable parameters, vegetation
Campus : Coimbatore
School : School of Engineering
Center : Computational Engineering and Networking
Department : Electronics and Communication
Verified : No
Year : 2019
Abstract : Classification of remotely sensed data requires the modelling of suitable image processing algorithms. The rise of machine learning systems upgraded the viability of satellite image applications. Using Convolutional Neural Networks (CNN), benchmark classification exactness can be accomplished for land cover grouping. Motivated by the concept of Normalized Difference Vegetation Index (NDVI), this paper utilizes only the red and near infrared (NIR) band information for classifying the publicly available SAT-4 and SAT-6 datasets. This is done, since NDVI computation requires only the two band (red and NIR) information and the classes involved in the dataset are types of vegetation. In this work, new deep learning architectures for three different networks (AlexNet, ConvNet, VGG) were proposed by hypertuning the network and the input as two band data. The modified architectures with the two band information along with reduced number of filters were trained and tested model manages to classify the images into different classes. The proposed architectures are compared against the existing architectures in terms of accuracy, precision and trainable parameters. The proposed architecture is found to perform equally efficient by retaining high accuracy with less number of trainable parameters, when compared against the the performance of benchmark deep learning architectures for satellite image classification. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.
Cite this Research Publication : A. Unnikrishnan, Sowmya V., and Dr. Soman K. P., “Deep learning architectures for land cover classification using red and near-infrared satellite images”, Multimedia Tools and Applications, vol. 78, no. 13, pp. 18379-18394 (IF: 2.313, CiteScore: 3.7, Q1- 80 percentile)., 2019.