结合不确定性准则和差分进化算法的高光谱数据分类方法
首发时间:2020-08-31
摘要:高光谱数据获取地物标签代价很高,对分类精度产生了很大的影响。半监督分类可以利用少量有标签样本和大量无标签样本,在与主动学习方法结合后取得了较好的分类效果,但是在精度和运算效率上仍然有改进的空间。为此,本文提出了一种结合多层次不确定性采样策略和自适应差分进化算法的半监督分类方法,首先通过多层次不确定性准则从无标签样本中选取信息量较高的样本和可以确定类别的样本,用后者和已知的有标签样本共同对前者进行标记,然后通过自适应差分进化算法寻优,扩充训练样本,最后使用新的训练样本集训练支持向量机分类器,对测试样本进行分类。实验结果表明,该方法能够充分利用样本信息,有效提高了小样本情况下高光谱数据的分类精度。
关键词: 高光谱 半监督分类 多层次不确定性准则 自适应差分进化 支持向量机
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Hyperspectral Data Classification Combined with Uncertainty Criterion and Differential Evolution Algorithm
Abstract:The cost of obtaining the feature labels for Hyperspectral image data is high,which has a great impact on the classification accuracy. The semi-supervised classification method can achieve better classification performance by using a small mount of supervisory information with given labels and a large amount of unsupervised information. After combining with the active learning method, a good classification effect is achieved, but there is still room to improve the accuracy and computational efficiency. Therefore, this paper proposes a semi-supervised classification method combining multi-level uncertainty sampling strategy and adaptive differential evolution algorithm. First, samples with higher information content and samples with determinable categories are selected from unlabeled samples through the multi-level uncertainty criterion, the latter and known labeled samples are used to label the former together, and then the adaptive differential evolution algorithm are used to execute optimization, expand the training samples, and finally use the new training sample set to train the support vector machine(SVM) classifier to classify the test samples. Experimental results show that this method can make full use of sample information and effectively improve the classification accuracy of hyperspectral images in the case of small samples.
Keywords: Hyperspectral semi-supervised classification multi-level uncertainty criterion self-adaptive differential evolution support vector machine(SVM)
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结合不确定性准则和差分进化算法的高光谱数据分类方法
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