Multisource Data Fusion Framework for Land Use/Land Cover Classification Using Machine Vision

Author:

Qadri Salman1ORCID,Khan Dost Muhammad1ORCID,Qadri Syed Furqan2,Razzaq Abdul3,Ahmad Nazir1,Jamil Mutiullah4ORCID,Nawaz Shah Ali1,Shah Muhammad Syed5ORCID,Saleem Khalid4,Awan Sarfraz Ahmad5ORCID

Affiliation:

1. Department of Computer Science & IT, The Islamia University of Bahawalpur, Punjab 63100, Pakistan

2. Key Laboratory of Photo-Electronic Imaging Technology and System, School of Computer Science, Beijing Institute of Technology (BIT), Beijing 100081, China

3. Department of Computer Science, NFC IET, Multan, Punjab 60000, Pakistan

4. Department of Computer Sciences, Quaid-i-Azam University, Islamabad 45320, Pakistan

5. Department of Computer Science, Virtual University of Pakistan, Lahore, Punjab 54000, Pakistan

Abstract

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.

Publisher

Hindawi Limited

Subject

Electrical and Electronic Engineering,Instrumentation,Control and Systems Engineering

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