1
Fakulti Pendidikan, UniversitiKebangsaan Malaysia
2
Universiti Utara Malaysia, Sintok, Kedah, Malaysia
3
Cluster of Education and Social Sciences, Open University Malaysia
Abstract
The current study involved two proposed algorithms: K_WIC and K_CIO. The algorithm K_WIC generates the good initialization center set and supports for speeding up execution of remote sensing image clustering. The algorithm K_CIO is used to cluster remote sensing images with adding context information of each pixel. The test results show that the algorithms K_WIC, K_CIO and K_WIC_CIO (a combination of K_WIC and K_CIO) can be used effectively for remote sensing image clustering. Besides, due to the nature of the Wavelet transform, the value domain of the output data is changed. Specifically, image doesn’t belonging to the domain [0,255]. Therefore, we proposed an improvement of Wavelet transformation to still ensure that the domain of the output data belongs to the domain [0,255], suitable for image data. In future work, we will continue to study the new context information and the new algorithms.
Faisal, N., Hamid, H., & Nordin, M. (2021). Machine Learning-Based Simulation In Remote Sensing Contexts. Int. J. of Aquatic Science, 12(1), 147-153.
MLA
NurAmalina Faisal; Hashibah Hamid; MohdNorazmi Nordin. "Machine Learning-Based Simulation In Remote Sensing Contexts". Int. J. of Aquatic Science, 12, 1, 2021, 147-153.
HARVARD
Faisal, N., Hamid, H., Nordin, M. (2021). 'Machine Learning-Based Simulation In Remote Sensing Contexts', Int. J. of Aquatic Science, 12(1), pp. 147-153.
VANCOUVER
Faisal, N., Hamid, H., Nordin, M. Machine Learning-Based Simulation In Remote Sensing Contexts. Int. J. of Aquatic Science, 2021; 12(1): 147-153.
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