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In this paper, we present an efficient approach based on multiple kernel learning (MKL) for mapping cloud thickness with MODIS and CloudSat data. In order to adapt the characteristics of radar data, we generalize a signal model from the gas imaging model, and the signal model provides a way for transforming the mapping of cloud thickness into a linear estimate problem. Then, considering the disadvantage...
Support Vector Machines (SVMs) is a popular classification and regression prediction tool that uses supervised machine learning theory to maximize the predictive accuracy. This paper focuses on the field programmable gate array (FPGA) implementation of a Support Vector Machine classification system. Owing to the advanced parallel calculation feature provided by FPGA, a fast data classification can...
In this paper, a novel spatial-spectral kernel method is proposed for classification in hyperspectral images. In this spatial-spectral multiple-kernel learning (S2MKL) method, extended morphological features as spatial information and originally spectral features are used as input features. Moreover, we implement a kernel EMP (KEMP) to better extract the spatial features. With those spatial and spectral...
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