The Comparison of Density-Based Clustering Approach among Different Machine Learning Models on Paddy Rice Image Classification of Multispectral and Hyperspectral Image Data

Shiuan Wan and Yi-Ping Wang
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Shiuan Wan: Information Technology, Ling Tung University, Taichung 40851, Taiwan
Yi-Ping Wang: Department of Soil and Environmental Sciences, National Chung Hsing University, Taichung 40851, Taiwan

Agriculture, 2020, vol. 10, issue 10, 1-17

Abstract: The analysis, measurement, and computation of remote sensing images often require enhanced unsupervised/supervised classification approaches. The goal of this study is to have a better understanding of (a) the classification performance of multispectral image and hyperspectral image data; (b) the classification performance of unsupervised and supervised models; and (c) the classification performance of feature selection among different models. More specifically, the multispectral images and hyperspectral images with high spatial resolution are well accepted for improving land use and classification. Hence, this study used multispectral images (WorldView-2) and hyperspectral images (CASI-1500) and focused on the classifiers K-means, density-based spatial clustering of applications with noise (DBSCAN), linear discriminant analysis (LDA), and back-propagation neural network (BPN). Then the feature selection (principle component analysis, PCA) on four classifiers is studied. The results show that the image material of CASI-1500 classification accuracy is slightly better than that of WorldView-2. The overall classification of BPN is the best, the overall data has a κ value of 0.89 and the overall accuracy is 97%. The DBSCAN presents a reality with good accuracy and the integrity of the thematic map. The DBSCAN can attain an accuracy of about 88% and save 95.1% of computational time.

Keywords: image classification; linear discriminant analysis; density-based clustering (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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