Support Vector Machine for Satellite Images Classification Using Radial Basis Function Kernel Method-Reference-Cited by-同舟云学术

Support Vector Machine for Satellite Images Classification Using Radial Basis Function Kernel Method

Published:2024 Issue: Volume: Page:301-312
ISSN:1865-0929
Container-title:Communications in Computer and Information Science
language:en
Short-container-title:

Author:

Mansor Nur Suhaili^ORCID,Awang Hapini^ORCID,Malami Sarkin Tudu Shehu^ORCID,Zolkafli Amirulikhsan,Taiye Mohammed Ahmed^ORCID,Maulana Hanhan^ORCID

Publisher

Springer Nature Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-99-9589-9_23

Reference22 articles.

1. Fahad, S., Tariq, A., Mousa, B.G., Mumtaz, F., Aslam, M.: Spatiotemporal variation in land use land cover in the response to local climate change using multispectral remote sensing data. Landsc. Urban Plan. 4, 1–24 (2022)

2. Manthira Moorthi, S., Misra, I., Kaur, R., Darji, N.P., Ramakrishnan, R.: Kernel-based learning approach for satellite image classification using support vector machine. In: 2011 IEEE Recent Advances in Intelligent Computational Systems, RAICS 2011, pp. 107–110 (2021). https://doi.org/10.1109/RAICS.2011.6069282

3. Tien Bui, D., Tuan, T.A., Klempe, H., Pradhan, B., Revhaug, I.: Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree. Landslides 13, 361–378 (2016). https://doi.org/10.1007/s10346-015-0557-6

4. Lü, G., et al.: Reflections and speculations on the progress in Geographic Information Systems (GIS): a geographic perspective. Int. J. Geogr. Inf. Sci. 33, 346–367 (2019). https://doi.org/10.1080/13658816.2018.1533136

5. Fan, K., Su, W.: Applications of fluorescence spectroscopy, RGB- and multispectral imaging for quality determinations of white meat: a review. MDPI 2, 1–30 (2022)