Publication Type : Journal Article, Conference Paper
Publisher : Lecture Notes in Electrical Engineering, Springer Verlag
Source : Lecture Notes in Electrical Engineering, Springer Verlag, Volume 500, p.161-170 (2019)
Url : https://www.scopus.com/inward/record.uri?eid=2-s2.0-85053621491&doi=10.1007%2f978-981-13-0212-1_17&partnerID=40&md5=b70cedda07a1aaa92176f3dce34c3448
ISBN : 9789811302114
Keywords : Classification (of information), Convolution, Convolutional networks, Convolutional neural network, image classification, Image classification algorithms, Land cover classification, Network architecture, Neural networks, Normalized difference vegetation index, Remote sensing, SAT-4, Satellite image classification, satellite imagery, Trainable parameters, vegetation
Campus : Coimbatore
School : School of Engineering
Center : Computational Engineering and Networking
Department : Electronics and Communication
Verified : No
Year : 2019
Abstract : The advent of neural networks has led to the development of image classification algorithms that are applied to different fields. In order to recover the vital spatial factor parameters, for example, land cover and land utilization, image grouping is most important in remote sensing. Recently, benchmark classification accuracy was achieved using convolutional neural networks (CNNs) for land cover classification. The most well-known tool which indicates the presence of green vegetation from multispectral pictures is the Normalized Difference Vegetation Index (NDVI). This chaper utilizes the success of the NDVI for effective classification of a new satellite dataset, SAT-4, where the classes involved are types of vegetation. As NDVI calculations require only two bands of information, it takes advantage of both RED- and NIR-band information to classify different land cover. The number and size of filters affect the number of parameters in convolutional networks. Restricting the aggregate number of trainable parameters reduces the complexity of the function and accordingly decreases overfitting. The ConvNet Architecture with two band information, along with a reduced number of filters, was trained, and high-level features obtained from a tested model managed to classify different land cover classes in the dataset. The proposed architecture, results in the total reduction of trainable parameters, while retaining high accuracy, when compared with existing architecture, which uses four bands. © Springer Nature Singapore Pte Ltd. 2019.
Cite this Research Publication : A. Unnikrishnan, Sowmya, and Dr. Soman K. P., “A two-band convolutional neural network for satellite image classification”, in Lecture Notes in Electrical Engineering, 2019, vol. 500, pp. 161-170.