Deep learning-based unsupervised domain adaptation (UDA) has shown potential in cross-scene hyperspectral image (HSI) classification. However, existing methods often experience reduced feature discriminability during domain alignment due to the difficulty of extracting semantic information from unlabeled target domain data. This challenge is exacerbated by ambiguous categories with similar material compositions and the underutilization of target domain samples. To address these issues, we propose a novel masked self-distillation domain adaptation (MSDA) framework, which enhances feature discriminability by integrating masked self-distillation (MSD) into domain adaptation. A class-separable adversarial training (CSAT) module is introduced to prevent misclassification between ambiguous categories by decreasing class correlation. Simultaneously, CSAT reduces the discrepancy between source and target domains through biclassifier adversarial training. Furthermore, the MSD module performs a pretext task on target domain samples to extract class-relevant knowledge. Specifically, MSD enforces consistency between outputs generated from masked target images, where spatial-spectral portions of an HSI patch are randomly obscured, and predictions produced based on the complete patches by an exponential moving average teacher. By minimizing consistency loss, the network learns to associate categorical semantics with unmasked regions. Notably, MSD is tailored for HSI data by preserving the samples’ central pixel and the object to be classified, thus maintaining class information. Consequently, MSDA extracts highly discriminative features by improving class separability and learning class-relevant knowledge, ultimately enhancing UDA performance. Experimental results on four datasets demonstrate that MSDA surpasses the existing state-of-the-art unsupervised domain adaptation methods for HSI classification. The code is available at https://github.com/Li-ZK/MSDA-2024.

We propose an image‐based face swapping algorithm, which can be used to replace the face in the reference image with the same facial shape and features as the input face. First, a face alignment is made based on a group of detected facial landmarks, so that the aligned input face and the reference face are consistent in size and posture. Secondly, an image warping algorithm based on triangulation is presented to adjust the reference face and its background according to the aligned input faces. In order to achieve more accurate face swapping, a face parsing algorithm is introduced to realize the accurate detection of the face‐ROIs, and then the face‐ROI in the reference image is replaced with the input face‐ROI. Finally, a Poisson image editing algorithm is adopted to realize the boundary processing and color correction between the replacement region and the original background, and then the final face swapping result is obtained. In the experiments, we compare our method with other face swapping algorithms and make a qualitative and quantitative analysis to evaluate the reality and the fidelity of the replaced face. The analysis results show that our method has some advantages in the overall performance of swapping effect.

Enhancing target domain discriminability is a key focus in Unsupervised Domain Adaptation (UDA) for HyperSpectral Image (HSI) classification. However, existing methods overlook bringing similar cross-domain samples closer together in the feature space to achieve the indirect transfer of source domain classification knowledge. To overcome this issue, we propose a Multi-Task Learning-based Domain Adaptation (MTLDA) method. MTLDA incorporates an inductive transfer mechanism into adversarial training, transferring the source classification knowledge to the target representation learning during the process of domain alignment. To enhance the target feature discriminability, we propose utilizing dual-domain contrastive learning to construct related tasks. A shared mapping network is employed to simultaneously perform Source domain supervised Contrastive Learning (SCL) and Target domain unsupervised Contrastive Learning (TCL), ensuring that similar samples across domains are positioned closely in the feature space, thereby improving the cross-scene HSI classification accuracy. Furthermore, we design a feature-level data augmentation method based on feature masking to assist contrastive learning tasks and generate more varied training data. Experimental results obtained from testing on three prominent HSI datasets demonstrate the MTLDA method’s superior efficacy in the realm of cross-scene HSI classification.

The feature-level domain alignment based on deep learning techniques has greatly improved the performance of unsupervised domain adaptation (UDA) for hyperspectral image (HSI) classification. However, most of these methods leverage convolutional neural networks to capture local features, overlooking the comparable spatial and spectral global information shared by both the source and target domains. To overcome this issue, we propose a local auxiliary spatial-spectral decoupling transformer (LASDT) network to ease the learning of global domain-invariant information. The spatial and spectral global features of HSIs are extracted through a dual-branch design, preventing the feature coupling of different dimensions. In order to compress the model's parameter search space, a local auxiliary global feature extraction strategy is devised. First, local prior constraints are introduced by extracting primitive features using convolutional intra-token embedding. Next, the extraction of global spatial and spectral information from these primitive features is effectively achieved using the self-attention mechanism. Finally, a dynamic feature fusion mechanism is devised that enables the model to focus on features more conducive to transfer while suppressing irrelevant features. By using only standard adversarial domain alignment, LASDT achieves the state-of-the-art performance, demonstrating the model's superior capability in UDA for HSI classification.