Despite the wide application of deep learning in hyperspectral classification, variations in data collection conditions can lead to domain shift between the training and testing datasets. Traditional hyperspectral classification methods are adversely affected by these distribution differences, resulting in poor generalization performance on the testing set. To overcome this challenge, we present an optimized unsupervised domain adaptation approach based on causal invariance. Our method assumes a causal relationship to reflect the effects of changes in class information and domain information on samples. Based on this causal relationship, we construct a network to separate class-related and domain-related features. To further reduce the negative transfer caused by distribution differences, our model introduces intra-class feature consistency. As a result, our method improves the performance of the model on the target domain. Experimental results on two public hyperspectral datasets demonstrate the superior effectiveness of our method.

Unsupervised domain adaptation (UDA) reduces domain shifts between distributions to enable model generalization to new scenarios. Adversarial domain adaptation (DA) is an effective approach that extracts domain-invariant features through adversarial learning, but such methods often neglect the influence of category differences on domain discrimination. To solve this problem, we construct a new unsupervised domain adaption hyperspectral image (HSI) classification method. The proposed method consists of two modules, namely, the pairing domain discrimination learning module and the multilevel mutual information maximization module. We propose to construct the sample pair as the input of the domain discriminator. We introduce a new label to the sample pair according to the labels of the two samples and use the relationship between samples to reduce the impact of sample category differences on domain discrimination. When extracting the shared features of the two domains, it will inevitably cause the loss of task-related information. This information is retained by maximizing the proposed multilevel mutual information. The experimental results on different datasets show the effectiveness of our method.

In hyperspectral cross-scene classification tasks, it is often challenging to obtain target domain samples during the training phase. Therefore, models need to be trained on one or multiple source domains and achieve good generalization performance on unknown target domains, known as domain generalization. The presence of domain shift limits the model’s generalization across different domains, while the unknown target domain makes it difficult to accurately characterize the distribution differences between domains. To address this issue, we propose a generalization network based on nonlinear sample generation. The network divides the sample features into invariant features and variant features and generates samples by applying nonlinear transformations to the variant features. To ensure the quality of the generated samples, we introduce contrastive learning into the model. It ensures consistency in similarity between the generated samples and the source samples while maintaining a certain degree of dissimilarity. Experiments conducted on four cross-domain adaptive scenarios demonstrate the superior performance of our proposed method.

Classifying high-dimensional hyperspectral image (HSI) with limited labeled samples is a difficult problem. One effective solution is to leverage knowledge from scenes with well-labeled image (the source domain) to aid training in the target domain. However, since the source and target domains have different category spaces, it is crucial to extract more discriminative features and address domain adaptation challenges. To tackle this issue, we propose a cross-scene classification method for HSIs via generative adversarial networks (GANs) in latent space (GLS). Our method employs autoencoders (AEs) to map the input data to a latent space, where the most effective feature representation is extracted and preserved by deep residual 3D convolutional neural networks (CNN). The unlabeled samples in the target domain are also utilized in the AE which ensure all the samples are considered. We leverage conditional adversarial domain adaptation to overcome the domain shift, and introduce maximum mean discrepancy loss to minimize distribution differences between the two domains, facilitating better domain distribution alignment. We tested our approach on three public datasets and demonstrated that it outperforms existing few-shot learning methods. Our results highlight the effectiveness of our classification method via GANs in latent space for HSIs, and show that it has potential for practical applications.

The traditional methods of hyperspectral image (HSI) classification are based on the sufficient labeled data. In real life, we often encounter that the target domain corresponding to the classification task has only a small amount of labeled data, but the source domain has enough labeled data. However, the distribution of the source domain is different from the distribution of the target domain. Thus, the labeled data of the source domain cannot be applied to the target domain directly. This paper proposes a new method to solve the cross-domain few shot problem of HSI classification. In the proposed method, the local spatial alignment and the spectral alignment are simultaneously introduced to transfer the knowledge from the source domain to the target domain. Besides, to extract the domain specific features, we balance the domain invariant features and the domain specific features by a weakly parameter-shared mechanism. The two modules together can narrow the distance between two domains and make the model perform well on the target domain. Experiments conducted on four different target domain data sets demonstrate the effectiveness of our method.