基于多尺度样本扩增的高光谱影像半监督分类

大量的训练样本可有效缓解模型过拟合，从而提高分类效果。在初始标记样本较少的情况下，开展借助不同尺度的同质区快速扩增大量高精度训练样本的实验，并利用初始标记样本和扩增样本训练支持向量机(Support Vector Machine, SVM)分类器，实现对高光谱数据的有效分类。该方法在Pavia University、Salinas和Indian Pines三种高光谱数据上均能获得大量高精度的训练样本，分类精度分别达到99%、99%和97%以上。实验结果表明，扩增的大量伪标签样本可以有效训练SVM分类器，提高分类效果。

Semi-Supervised Classification of Hyperspectral Images Based on Multi-Scale Sample Amplification

A large number of training samples can effectively alleviate the overfitting of the model and improve the classification effect. A lot of high-precision training samples are rapidly amplified by using homogenous regions of different scales. The support vector machine classifier is trained with the initial labeled samples and amplified samples to achieve the effective classification of hyperspectral data. The majority of high-precision training samples based on Pavia University data, Salinas data and Indian Pines data can be obtained by this method, and the accuracy is above 99%, 99% and 97% respectively. The experiment results show that the large number of pseudo-label samples amplified by the proposed method can effectively train the SVM classifier and improve the classification effect.