针对高光谱遥感图像分类时有标注的源域训练数据与无标注目标域数据分布不一致的问题，提出基于部分最优传输的无监督领域自适应方法，实现对处于不同数据分布的高光谱遥感地物像素级分类。利用深度卷积神经网络将样本映射到潜在高维空间，根据部分最优传输理论建立样本传输方案，最小化域间分布差异，构建适配模型。采用类感知采样技术和质量分数因子自适应调整策略，促进域间类别对齐，建立全局最优传输。在两组公开高光谱遥感图像数据集上进行实验，从总体分类精度OA（%）、类别平均分类精度AA（%）、分类一致性检验Kappa（×100）等3个评价指标对像素分类结果量化比较。实验结果显示，在两组迁移任务上，相较于仅使用源域数据的基线模型，总体分类准确率分别提升2.21%和2.75%，相较于原始最优传输策略提升1.71%和2.01%，表明模型能够有效提升高光谱遥感影像中像素级地物的分类精度。

To improve the performance of few-shot classification, we present a general and flexible method named Multi-Scale Attention and Domain Adaptation Network (MADA). Firstly, to tackle the problem of limited samples, a masked autoencoder is used to image augmentation. Moreover, it can be inserted as a plug-and-play module into a few-shot classification. Secondly, the multi-scale attention module can adapt feature vectors extracted by embedding function to the current classification task. Multi-scale attention machine strengthens the discriminative image region by focusing on relating samples in both base class and novel class, which makes prototypes more accurate. In addition, the embedding function pays attention to the task-specific feature. Thirdly, the domain adaptation module is used to address the domain shift caused by the difference in data distributions of the two domains. The domain adaptation module consists of the metric module and the margin loss function. The margin loss pushes different prototypes away from each other in the feature space. Sufficient margin space in feature space improves the generalization performance of the method. The experimental results show the classification accuracy of the proposed method is 67.45% for 5-way 1-shot and 82.77% for 5-way 5-shot on the miniImageNet dataset. The classification accuracy is 70.57% for 5-way 1-shot and 85.10% for 5-way 5-shot on the tieredImageNet dataset. The classification accuracy of our method is better than most previous methods. After dimension reduction and visualization of features by using t-SNE, it can be concluded that domain drift is alleviated, and prototypes are more accurate. The multi-scale attention module enhanced feature representations are more discriminative for the target classification task. In addition, the domain adaptation module improves the generalization ability of the model.

近年来，基于深度学习的目标检测技术在机器人、自动驾驶和交通监控等领域有着广泛的应用。然而，由于训练集和测试集样本分布偏差的原因，将现成的预训练检测器应用到实际开放场景时通常会出现明显性能下降。针对该问题提出了一种频域内的领域自适应方法，利用离散余弦变换的频域能量集中特性，通过在频域内对少数重要频率系数进行处理，实现了面向目标检测的领域自适应，降低了对存储和计算资源的要求并减少了领域差异。该方法可以分为两个阶段：第一阶段使用无监督图像转换方式，将源域已标注的训练数据向目标域作转换；第二阶段采用基于对抗的领域自适应方法训练目标检测模型，对转换后的训练数据与目标域内的数据作特征适配。针对不同天气场景的目标识别实验表明：所提的频域内领域自适应方法在4种领域自适应对比算法中排名第一，与仅用源域数据训练的模型相比，mAP值提升了33.9%。