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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Shiuan Wan & Yi-Ping Wang, 2020. "The Comparison of Density-Based Clustering Approach among Different Machine Learning Models on Paddy Rice Image Classification of Multispectral and Hyperspectral Image Data," Agriculture, MDPI, vol. 10(10), pages 1-17, October.
    • Handle: RePEc:gam:jagris:v:10:y:2020:i:10:p:465-:d:425804
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    References listed on IDEAS

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    1. Yong Tian & Bojia Ye & Lili Wan & Minhao Yang & Dawei Xing, 2019. "Restricted Airspace Unit Identification Using Density-Based Spatial Clustering of Applications with Noise," Sustainability, MDPI, vol. 11(21), pages 1-15, October.
    2. Peres-Neto, Pedro R. & Jackson, Donald A. & Somers, Keith M., 2005. "How many principal components? stopping rules for determining the number of non-trivial axes revisited," Computational Statistics & Data Analysis, Elsevier, vol. 49(4), pages 974-997, June.
    3. Gupta, Jatinder N.D. & Stafford, Edward Jr., 2006. "Flowshop scheduling research after five decades," European Journal of Operational Research, Elsevier, vol. 169(3), pages 699-711, March.
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    Cited by:

    1. Tsu Chiang Lei & Shiuan Wan & You Cheng Wu & Hsin-Ping Wang & Chia-Wen Hsieh, 2022. "Multi-Temporal Data Fusion in MS and SAR Images Using the Dynamic Time Warping Method for Paddy Rice Classification," Agriculture, MDPI, vol. 12(1), pages 1-23, January.

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