IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v10y2020i10p465-d425804.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/10/10/465/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/10/10/465/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Claudio Morana, 2014. "Factor Vector Autoregressive Estimation of Heteroskedastic Persistent and Non Persistent Processes Subject to Structural Breaks," Working Papers 273, University of Milano-Bicocca, Department of Economics, revised May 2014.
    2. Kim, Hyun Hak & Swanson, Norman R., 2018. "Mining big data using parsimonious factor, machine learning, variable selection and shrinkage methods," International Journal of Forecasting, Elsevier, vol. 34(2), pages 339-354.
    3. Joy R. Petway & Yu-Pin Lin & Rainer F. Wunderlich, 2019. "Analyzing Opinions on Sustainable Agriculture: Toward Increasing Farmer Knowledge of Organic Practices in Taiwan-Yuanli Township," Sustainability, MDPI, vol. 11(14), pages 1-27, July.
    4. Jonas Eberle & Renier Myburgh & Dirk Ahrens, 2014. "The Evolution of Morphospace in Phytophagous Scarab Chafers: No Competition - No Divergence?," PLOS ONE, Public Library of Science, vol. 9(5), pages 1-16, May.
    5. Johann Fuchs & Doris Söhnlein & Brigitte Weber & Enzo Weber, 2018. "Stochastic Forecasting of Labor Supply and Population: An Integrated Model," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 37(1), pages 33-58, February.
    6. Yeung, Wing-Kwan & Choi, Tsan-Ming & Cheng, T.C.E., 2011. "Supply chain scheduling and coordination with dual delivery modes and inventory storage cost," International Journal of Production Economics, Elsevier, vol. 132(2), pages 223-229, August.
    7. Hauck, Jana & Suess-Reyes, Julia & Beck, Susanne & Prügl, Reinhard & Frank, Hermann, 2016. "Measuring socioemotional wealth in family-owned and -managed firms: A validation and short form of the FIBER Scale," Journal of Family Business Strategy, Elsevier, vol. 7(3), pages 133-148.
    8. B-J Joo & Y-D Kim, 2009. "A branch-and-bound algorithm for a two-machine flowshop scheduling problem with limited waiting time constraints," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(4), pages 572-582, April.
    9. Volker G. Kuppelwieser & Aleksa-Carina Putinas & Marina Bastounis, 2019. "Toward Application and Testing of Measurement Scales and an Example," Sociological Methods & Research, , vol. 48(2), pages 326-349, May.
    10. Waldherr, Stefan & Knust, Sigrid & Briskorn, Dirk, 2017. "Synchronous flow shop problems: How much can we gain by leaving machines idle?," Omega, Elsevier, vol. 72(C), pages 15-24.
    11. Leise Kelli de Oliveira & Carla de Oliveira Leite Nascimento & Paulo Renato de Sousa & Paulo Tarso Vilela de Resende & Francisco Gildemir Ferreira da Silva, 2019. "Transport Service Provider Perception of Barriers and Urban Freight Policies in Brazil," Sustainability, MDPI, vol. 11(24), pages 1-17, December.
    12. Ard H. J. den Reijer & Pieter W. Otter & Jan P. A. M. Jacobs, 2024. "An heuristic scree plot criterion for the number of factors," Statistical Papers, Springer, vol. 65(6), pages 3991-4000, August.
    13. Figielska, Ewa, 2014. "A heuristic for scheduling in a two-stage hybrid flowshop with renewable resources shared among the stages," European Journal of Operational Research, Elsevier, vol. 236(2), pages 433-444.
    14. David Juárez-Varón & Victoria Tur-Viñes & Alejandro Rabasa-Dolado & Kristina Polotskaya, 2020. "An Adaptive Machine Learning Methodology Applied to Neuromarketing Analysis: Prediction of Consumer Behaviour Regarding the Key Elements of the Packaging Design of an Educational Toy," Social Sciences, MDPI, vol. 9(9), pages 1-23, September.
    15. Gang Li & Chu Zhang, 2010. "On the Number of State Variables in Options Pricing," Management Science, INFORMS, vol. 56(11), pages 2058-2075, November.
    16. Edoardo Saccenti & Marieke E. Timmerman, 2017. "Considering Horn’s Parallel Analysis from a Random Matrix Theory Point of View," Psychometrika, Springer;The Psychometric Society, vol. 82(1), pages 186-209, March.
    17. Matthias Bultmann & Sigrid Knust & Stefan Waldherr, 2018. "Flow shop scheduling with flexible processing times," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 40(3), pages 809-829, July.
    18. repec:jss:jstsof:46:i04 is not listed on IDEAS
    19. Dray, Stephane, 2008. "On the number of principal components: A test of dimensionality based on measurements of similarity between matrices," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 2228-2237, January.
    20. Li, Wei & Nault, Barrie R. & Ye, Honghan, 2019. "Trade-off balancing in scheduling for flow shop production and perioperative processes," European Journal of Operational Research, Elsevier, vol. 273(3), pages 817-830.
    21. repec:dgr:rugsom:14008-eef is not listed on IDEAS
    22. Psaradakis, Zacharias & Vávra, Marián, 2014. "On testing for nonlinearity in multivariate time series," Economics Letters, Elsevier, vol. 125(1), pages 1-4.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jagris:v:10:y:2020:i:10:p:465-:d:425804. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.