An innovative multi-kernel learning algorithm for hyperspectral classification. (October 2019)

• Record Type: Journal Article
• Authors: Li, Fei; Lu, Huchuan; Zhang, Pingping
• Is Part Of: Computers & electrical engineering. Volume 79(2019)
• Publication Date: 2019-10
• Subjects: Hyperspectral -- Kernel learning -- Classification -- Adaptive boosting; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2019.106456
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: Many studies show that support vector machine (SVM) techniques have gotten superior performance in hyperspectral classification. SVM classifier's improvement was fulfilled by the combination of multiple kernel tricks. Multi-kernel learning (MKL) algorithms have been used in hyperspectral processing. Combining SVM and MKL, we propose a novel method for hyperspectral classification. It adopts a boost algorithm rather than the global optimization method to search for the optimal combination of kernel SVMs. Experiments were carried out on several reflectance spectra of plankton and two hyperspectral remote sensing images to verify the approach. It turns out that the proposed method achieved better classification performance than some conventional algorithms.