| 摘 要: 高光谱图像包含光谱和空间信息，这增加了其在分类与识别方面的难度。特征学习作为高光谱图像分类技术之一，能较好地提取图像中包含的特征。针对经典极限学习机算法难以较好地提取光谱特征，引入特征学习技术，提出了一种基于判别信息的复合核极限学习机(CKELM-L)方法。CKELM-L通过最大化类间矩阵与最小化类内矩阵，使投影后的低维数据同类越近而异类越远。实验结果表明，所提方法保留了更好的光谱特征，计算复杂度低且实现了出色的可分离性。 |
| 关键词: 极限学习机；高光谱图像分类；线性判别分析；特征学习 |
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中图分类号: TP391
文献标识码: A
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| 基金项目: 国家自然科学基金项目(61772250)；辽宁省自然科学基金(20170540574)；辽宁省教育厅科学研究项目(LJ2019014). |
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| Composite Kernel Extreme Learning Machine Algorithm based on Discriminant Information |
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MA Siyu, LIU Deshan, YAN Deqin, DING Yimin
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(School of Computer and Information Technology, Liaoning Normal University, Dalian 116081, China)
MasiyuV@163.com; deshanliu@yeah.net; yandeqin@163.com; 18340817981@163.com
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| Abstract: Hyperspectral images contain spectral and spatial information, which increases the difficulty of classification and recognition. Feature learning, as one of the hyperspectral image classification techniques, can better extract features contained in the image. Aiming at the difficulty of classical extreme learning machine algorithms in extracting spectral features, this paper introduced feature learning technology, and proposes a composite kernel extreme learning machine(CKELM-L) method based on discriminant information. CKELM-L maximizes the between-class matrix and minimizes the intra-class matrix, so that the projected low-dimensional data is closer to the same class and farther away from the different class. Experimental results show that the proposed method retains better spectral features, low computational complexity and achieves excellent separability. |
| Keywords: extreme learning machine; hyperspectral image classification; linear discriminant analysis; feature learning |
