利用已有图像的标记样本对新的高光谱图像分类面临光谱偏移带来的分类性能差的问题。基于特征表示的域适应方法通过学习域不变特征来解决这个问题。然而现有方法难以同时将多个类的样本对齐，在对齐多类样本的同时又忽略了类间混合对可分性造成的影响。本文提出了一种类别独立的域适应分类方法。首先，为每个地物类别构造一个独立的降维子空间，在多个类别独立的子空间中对齐源域和目标域的样本。然后，在每个类别独立子空间中，利用对齐样本学习出目标域样本的后验概率。接着，融合所有类别独立子空间得到的后验概率得到分类标签，目的是增加后验概率的可信度。最后，利用空间先验平滑分类标签后将其作为伪标签用于迭代学习，更新类别独立子空间和目标域的分类结果。另外，本文还设计了代表性样本选择策略，有利于学习出更具共性的特征表达子空间。在两个真实的高光谱数据集上的实验结果表明，本文算法比原始的联合域适应算法的最近邻分类精度分别提升了9.56%和18.45%。

The performance of hyperspectral image supervised classification largely depends on the quantity and quality of labeled samples. However, labeling hyperspectral data is a difficult and time-consuming procedure. When the number of labeled samples is too low, supervised classifiers suffer from a high risk of overfitting problem. In this paper, we address the problem of unsupervised domain adaptation, where labeled samples of old images (source domain) are used to classify new hyperspectral data (target domain). The existing domain adaptation methods aim at learning the domain-invariant features in a new space. However, a large proportion of these methods align only the overall statistics of the two domains, while neglecting to reduce the spectral shifts in each class. Other methods are expected to align every class of the source and target domains simultaneously. On the one hand, these methods will be misled by wrong label information. On the other hand, aligning multiple classes may reduce data separability, due to the mixture of samples from different classes. In this paper, we propose a class-independent domain adaptation algorithm for hyperspectral image classification. Our method first constructs an independent subspace for each class and then aligns the samples of two domains in this subspace. In each class-independent subspace, the posterior probabilities of the target domain are learned by using the aligned samples. Then, the posterior probabilities obtained from multiple subspaces are fused to produce the classification labels, aiming at increasing the confidence of results. Finally, the classification labels are smoothed and used as pseudolabels for iterative learning. Moreover, we also present a strategy of selecting the representative sample set to obtain better subspaces. Experimental results on two real hyperspectral datasets show that our proposed method has high classification performance. Compared with the joint domain adaptation algorithm, the accuracy of our proposed method with the nearest neighbor classifier is improved by 9.56% on Honghu data and 18.45% on Wen-County data.