高光谱图像数据的多尺度多核SVM分类

Multi-scale Multiple Kernel SVM Classification for Hyperspectral Imagery Data

针对采用单核学习支持向量机不能很好地处理样本分布不均衡、复杂多变的高光谱图像数据的分类问题,提出一种结合采样技术和多核学习的高光谱图像数据的分类方法。该方法先对支持向量机模型中的少数类支持向量过采样而不是对训练样本采样以达到数据平衡,然后利用加权求和核的方式进行多尺度多核学习,通过梯度下降算法实现权系数的求解建立多核支持向量机,最后利用一系列二分类器组合解决多类分类问题。实验结果表明,该方法与传统的支持向量机分类方法相比地物的总体分类精度(OA)提高了4.07%,平均分类精度(AA)提高了9.62%。

Aiming at support vector machine （SVM） using single kernel learning not handling the classification problem of hyperspectral imagery data that the sample distribution is irregular and complex, a hyperspectral imagery data classification method based on sampling strategy and muhiple kernel support vector machine is proposed in this paper. Firstly the method does sampling referring to the minority support vectors （SVs） rather than the training data to provide a balanced distribution during multiple kernel support vector machine mode, and then uses the weighted sum approach to multiple kernel learning（MKL） and optimizes parameters by gradient descent algorithm. Finally, a series of two-class classifiers are used to achieve the multi-class classification. Experimental results show that overall classification accuracy increased by 4.07%, average classification accuracy increased by 9.62% compared with the traditional SVM.