The hyperspectral image (HSI) has the characteristics of high resolution, a large amount of data, and a high correlation of bands. In the many HSI processing technologies, image classification is the most basic one. Supervised classification is the most effective and common classification method. However, to achieve the ideal classification effect, supervised classification needs a large number of labeled samples, which requires a lot of time and labor. To solve this problem, we combine active learning (AL) and transfer learning and propose an iterative weighted framework based on active transfer learning. First, we solve the optimal reconstruction matrix and projection matrix by minimizing the reconstruction error. Then, we project labeled samples from the source and target domains into the common subspace. In the iteration of the common subspace, the classifier performance will be better with the increase of iteration number. In each iteration, the weighted strategy is adopted to weigh the samples of the source domain. In this way, valuable source domain labeled samples will get a larger weight, so as to help the classification of target domain samples better. At the same time, AL is used to screen out a certain number of samples of the target domain for manual labeling, which is added to the labeled samples set. Experiments on three data sets demonstrate the effectiveness and reliability of the proposed method.

中文翻译：

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迭代加权主动迁移学习高光谱图像分类

高光谱图像（HSI）具有分辨率高、数据量大、波段相关性高等特点。在众多的 HSI 处理技术中，图像分类是最基本的一种。监督分类是最有效、最常用的分类方法。然而，要达到理想的分类效果，监督分类需要大量的标记样本，需要大量的时间和人力。为了解决这个问题，我们将主动学习（AL）和迁移学习相结合，提出了一个基于主动迁移学习的迭代加权框架。首先，我们通过最小化重构误差来求解最优重构矩阵和投影矩阵。然后，我们将来自源域和目标域的标记样本投影到公共子空间中。在公共子空间的迭代中，随着迭代次数的增加，分类器的性能会更好。在每次迭代中，采用加权策略对源域的样本进行加权。这样，有价值的源域标记样本将获得更大的权重，从而更好地帮助目标域样本的分类。同时使用AL筛选出一定数量的目标域样本进行人工标注，加入到标注样本集中。在三个数据集上的实验证明了所提出方法的有效性和可靠性。从而更好地帮助目标域样本的分类。同时使用AL筛选出一定数量的目标域样本进行人工标注，加入到标注样本集中。在三个数据集上的实验证明了所提出方法的有效性和可靠性。从而更好地帮助目标域样本的分类。同时使用AL筛选出一定数量的目标域样本进行人工标注，加入到标注样本集中。在三个数据集上的实验证明了所提出方法的有效性和可靠性。