ABSTRACT This paper presents a novel Semi-Supervised Domain Adaptation (SSDA) method for hyperspectral image classification. Although, SSDA methods are useful when the number of the training samples are limited, but still encounter some problems. First, the traditional SSDA methods based on kernel prediction model consider a predefined kernel for both domains without using the target samples into classifier structure, which makes the challenges for classification of the noisy and complex dataset in the target domain. Second, the previous SSDA methods only measured and decreased the Maximum Mean Discrepancy (MMD) between source and target domains in order to decrease the distribution discrepancy, which ignores the discriminative information in both domains. To solve these issues, we propose a Robust Transfer Joint Matching Distribution (RTJMD) based on both the classification error and the distribution discrepancy minimization principle. We present a generalized multi-kernel model by incorporating two Fredholm integral to find an optimal kernel. Then, we propose a Regularized Extended Maximum Distribution Discrepancy (REMDD) metric in Reproduced Kernel Hilbert Space (RKHS), which considers both the extended maximum mean discrepancy and the extended maximum variance discrepancy with Multiple Kernel Learning (MKL) between two domains. The experimental results with two benchmark datasets show that the proposed RTJMD improves the classification accuracy and generalization capabilities compared to conventional SSDA approaches even in noisy and complex cases.

中文翻译：

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用于高光谱图像分类的半监督域适应中的鲁棒转移联合匹配分布

摘要 本文提出了一种用于高光谱图像分类的新型半监督域适应 (SSDA) 方法。虽然 SSDA 方法在训练样本数量有限的情况下很有用，但仍然会遇到一些问题。首先，传统的基于核预测模型的 SSDA 方法在两个域都考虑了一个预定义的核，而没有将目标样本用于分类器结构，这给目标域中噪声和复杂数据集的分类带来了挑战。其次，之前的 SSDA 方法仅测量和减少源域和目标域之间的最大平均差异 (MMD) 以减少分布差异，这忽略了两个域中的判别信息。为了解决这些问题，我们提出了一种基于分类误差和分布差异最小化原则的鲁棒转移联合匹配分布（RTJMD）。我们通过结合两个 Fredholm 积分来找到一个最佳内核，从而提出了一个广义的多内核模型。然后，我们在再生核希尔伯特空间 (RKHS) 中提出了正则化扩展最大分布差异 (REMDD) 度量，该度量同时考虑了两个域之间的扩展最大平均差异和多核学习 (MKL) 扩展最大方差差异。两个基准数据集的实验结果表明，即使在嘈杂和复杂的情况下，与传统的 SSDA 方法相比，所提出的 RTJMD 也提高了分类精度和泛化能力。