Multisource Data Fusion Framework for Land UseLand Cover Classification Using Machine Vision
Joint Authors
Qadri, Salman
Khan, Dost Muhammad
Shah Muhammad, Syed
Ahmad, Sarfraz
Jamil, Mutiullah
Qadri, Syed Furqan
Razzaq, Abdul
Ahmad, Nazir
Nawaz Shah, Ali
Saleem, Khalid
Source
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-09-11
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
Data fusion is a powerful tool for the merging of multiple sources of information to produce a better output as compared to individual source.
This study describes the data fusion of five land use/cover types, that is, bare land, fertile cultivated land, desert rangeland, green pasture, and Sutlej basin river land derived from remote sensing.
A novel framework for multispectral and texture feature based data fusion is designed to identify the land use/land cover data types correctly.
Multispectral data is obtained using a multispectral radiometer, while digital camera is used for image dataset.
It has been observed that each image contained 229 texture features, while 30 optimized texture features data for each image has been obtained by joining together three features selection techniques, that is, Fisher, Probability of Error plus Average Correlation, and Mutual Information.
This 30-optimized-texture-feature dataset is merged with five-spectral-feature dataset to build the fused dataset.
A comparison is performed among texture, multispectral, and fused dataset using machine vision classifiers.
It has been observed that fused dataset outperformed individually both datasets.
The overall accuracy acquired using multilayer perceptron for texture data, multispectral data, and fused data was 96.67%, 97.60%, and 99.60%, respectively.
American Psychological Association (APA)
Qadri, Salman& Khan, Dost Muhammad& Qadri, Syed Furqan& Razzaq, Abdul& Ahmad, Nazir& Jamil, Mutiullah…[et al.]. 2017. Multisource Data Fusion Framework for Land UseLand Cover Classification Using Machine Vision. Journal of Sensors،Vol. 2017, no. 2017, pp.1-8.
https://search.emarefa.net/detail/BIM-1186882
Modern Language Association (MLA)
Qadri, Salman…[et al.]. Multisource Data Fusion Framework for Land UseLand Cover Classification Using Machine Vision. Journal of Sensors No. 2017 (2017), pp.1-8.
https://search.emarefa.net/detail/BIM-1186882
American Medical Association (AMA)
Qadri, Salman& Khan, Dost Muhammad& Qadri, Syed Furqan& Razzaq, Abdul& Ahmad, Nazir& Jamil, Mutiullah…[et al.]. Multisource Data Fusion Framework for Land UseLand Cover Classification Using Machine Vision. Journal of Sensors. 2017. Vol. 2017, no. 2017, pp.1-8.
https://search.emarefa.net/detail/BIM-1186882
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1186882