Hyperspectral image (HSI) classification is a challenging problem due to the high dimensional features, high intra-class variance, and limited prior information, and the classification is the basis for HSI applications. Active learning (AL) and semisupervised learning (SSL) are two promising approaches in the HSI classification. In AL, the traditional entropy query-by-bagging (EQB) algorithm only pays attention on uncertainty and ignore the diversity among the samples. Therefore, we propose averaged normalized entropy query-by-bagging (anEQB) algorithm. Meanwhile, the collaborative active learning and semisupervised learning framework (CASSL) may invoke many wrong pseudolabels and deteriorate the classification performance. To make up for the deficiency of CASSL, we complement different AL algorithms to constitute a multiple filtering mode semisupervised learning framework (MFMSLF). To further study, we introduce syncretic secondary filtering mode into multiple verification semisupervised framework and thus constitute a multiple secondary filtering mode semisupervised verification framework (MSFMSVF). We evaluate the performance of anEQB, MFMSLF, and MSFMSVF on different hyperspectral data sets and compare them with other state-of-the-art HSI classification methods. Numerical experimental results reveal the superior classification performance of anEQB, MFMSLF, and MSFMSVF, respectively. Experimental results also demonstrate that exploring the information and diversity of the samples from different criterion can improve the classification performance of the collaborative framework.

中文翻译：

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高光谱图像的联合多模式协同分类算法

由于高维特征，高类内差异和有限的先验信息，高光谱图像（HSI）分类是一个具有挑战性的问题，而分类是HSI应用程序的基础。主动学习（AL）和半监督学习（SSL）是HSI分类中的两种有前途的方法。在AL中，传统的袋式熵查询（EQB）算法仅关注不确定性，而忽略了样本之间的多样性。因此，我们提出了袋装平均归一化熵查询（anEQB）算法。同时，协作式主动学习和半监督学习框架（CASSL）可能会调用许多错误的伪标签，从而降低分类性能。为了弥补CASSL的不足，我们补充了不同的AL算法，以构成多重过滤模式半监督学习框架（MFMSLF）。为了进一步研究，我们将同步二次过滤模式引入了多重验证半监督框架，从而构成了多重二次过滤模式半监督验证框架（MSFMSVF）。我们评估了anEQB，MFMSLF和MSFMSVF在不同的高光谱数据集上的性能，并将其与其他最新的HSI分类方法进行了比较。数值实验结果分别显示了anEQB，MFMSLF和MSFMSVF的优异分类性能。实验结果还表明，从不同标准中探索样本的信息和多样性可以提高协作框架的分类性能。