Classifying hyperspectral remote sensing images across different scenes has recently emerged as a significant challenge. When only historical labeled images (source domain, SD) are available, it is crucial to leverage these images effectively to train a model with strong generalization ability that can be directly applied to classify unseen samples (target domain, TD). To address these challenges, this paper proposes a novel single-domain generalization (SDG) network, termed the domain-aware adversarial domain augmentation network (DADAnet) for cross-scene hyperspectral image classification (HSIC). DADAnet involves two stages: adversarial domain augmentation (ADA) and task-specific training. ADA employs a progressive adversarial generation strategy to construct an augmented domain (AD). To enhance variability in both spatial and spectral dimensions, a domain-aware spatial-spectral mask (DSSM) encoder is constructed to increase the diversity of the generated adversarial samples. Furthermore, a two-level contrastive loss (TCC) is designed and incorporated into the ADA to ensure both the diversity and effectiveness of AD samples. Finally, DADAnet performs supervised learning jointly on the SD and AD during the task-specific training stage. Experimental results on two public hyperspectral image datasets and a new Hangzhouwan (HZW) dataset demonstrate that the proposed DADAnet outperforms existing domain adaptation (DA) and domain generalization (DG) methods, achieving overall accuracies of 80.69%, 63.75%, and 87.61% on three datasets, respectively.

Due to the complex acquisition environment and scarcity of labels, domain adaptation (DA) techniques are widely applied to cross-scenario hyperspectral image (HSI) classification to achieve more precise labeling. Many existing approaches mainly rely on convolutional neural networks (CNNs) to capture local spatial contextual relationships, supplemented by graph convolutional networks (GCNs) for long-range modeling. However, GCNs usually require full batch training and fixed initial graph structures, which significantly limits the exploration of topological structures. To address this, a multi-contrastive and dynamic topological matching network (MCDTM) is introduced to accomplish cross-domain HSI classification. Unlike fixed graph construction methods, mini-batches of samples are utilized to construct dynamic subgraphs within the source and target domains, respectively, with locally extracted features from CNNs serving as the basis for graph construction. More importantly, as the model is optimized and the domain gap narrows, the dynamic graph structure is adaptively adjusted according to the evolving samples, thereby boosting the accuracy of the graph and enhancing the discriminative power of the model. Moreover, the integration of weighted multi-positive contrastive learning and graph matching achieves distribution alignment and graph alignment, enhancing the model's capacity to distinguish and align complex patterns in HSI. Experimental results in three tasks show that the MCDTM surpasses several advanced DA methods, achieving impressive accuracies of 80.10%, 70.01%, and 94.17% on the Houston, HyRank, and YC-YC tasks, thereby showcasing its superior performance.

Recently, the adversarial domain adaptation (ADA) methods have been widely investigated and applied in cross-domain hyperspectral image (HSI) classification. However, most ADA algorithms aim to align the cross-domain distribution without focusing on the class separability of the aligned target features and the information of samples within the domain. To address these issues, a new ADA framework based on calibrated prototype and dynamic instance convolution (CPDIC) is proposed in this paper for cross domain HSI classification. The CPDIC is composed of a generator, a calibrated discriminator and a classifier. The generator includes a static 3D convolutional network (SCN) and a dynamic instance convolutional network (DICN), where the SCN is used to extract coarse-grained features of HSI and the DICN can extract sample-specific fine-grained features using instance convolutions generated from dynamic instance convolution kernel generation (DCKG) module. As for the generator, the static and dynamic interactive feature extraction network extracts robust domain-invariant features with discriminability. The calibrated discriminator aligns the marginal distribution between domains and calibrate the predicted pseudo labels of target domain. For classification, a calibrated prototype loss (CPL) is introduced to align the class distribution across domains. The results of three cross-domain HSI classification tasks show that the proposed CPDIC outperforms existing unsupervised domain adaptation (UDA) algorithms.

In recent years, with the continuous development of deep learning (DL), neural networks have demonstrated good results in large-sample hyperspectral image (HSI) classification. However, in practice, labels are often limited. In order to use fewer labeled samples without degrading the classification performance, this letter proposes a new semi-supervised classification method named prototype and active learning network (PALN), which integrates DL, active learning (AL) and prototype learning (PL) into a framework. After training the DL network with a small number of available labels, samples with high uncertainty are selected by AL to assign true labels, while samples more similar with prototypes are chosen by PL with their pseudo labels, and all selected samples are appended to the training set for the next training. Compared with existing classification methods, our method achieves good performance on two hyperspectral datasets.

Deep learning has been extensively used for hyperspectral image (HSI) classification with significant success, but the classification of high-dimensional HSI datasets with a limited amount of labeled samples is still a great challenge. Few-shot learning (FSL) has shown excellent performance in solving small-sample classification problems. However, most of the existing FSL methods usually suffer from the prototype instability and domain shift. In order to address these problems, this paper proposes a category-specific prototype self-refinement contrastive learning (CPSRCL) method for cross-domain FSL of HSIs. Our method uses a supervised contrastive learning (SCL) strategy to promote intra-class compactness and inter-class dispersion of features in the metric space. To stabilize and refine the prototypes of the support set, a category-specific prototype self-refinement (CSPSR) module is designed to adaptively learn different updating rules for different category prototypes using rich labeled information in the query set. Furthermore, a local discriminative domain adaptation (LDDA) method is constructed to align the global distribution between source and target domains while preserving domain-specific discriminative information. Experimental results on four public HSI datasets demonstrate that CPSRCL outperforms existing FSL and deep learning methods for HSI classification.

Recently, prototypical network based few-shot learning (FSL) has been introduced for small-sample hyperspectral image (HSI) classification and shown good performance. However, existing prototypical-based FSL methods have two problems: prototype instability and domain shift between training and testing datasets. To solve these problems, we propose a refined prototypical contrastive learning network for few-shot learning (RPCL-FSL) in this paper, which incorporates supervised contrastive learning and FSL into an end-to-end network to perform small-sample HSI classification. To stabilize and refine the prototypes, RPCL-FSL imposes triple constraints on prototypes of the support set, i.e., contrastive learning (CL), self-calibration (SC) and cross-calibration (CC) based constraints. The CL module imposes internal constraint on the prototypes aiming to directly improve the prototypes using support set samples in the CL framework, and the SC and CC modules impose external constraints on the prototypes by using the prediction loss of support set samples and the query set prototypes, respectively. To alleviate domain shift in the FSL, a fusion training strategy is designed to reduce the feature differences between training and testing datasets. Experimental results on three HSI datasets demonstrate that the proposed RPCL-FSL outperforms existing state-of-the-art deep learning and FSL methods.

Graph convolutional network (GCN) has recently attracted great attention in hyperspectral image (HSI) classification due to its strong ability to aggregate information of neighborhood nodes. However, a GCN model usually suffers from the over-smoothing problem (i.e., all nodes’ representations converge to a stationary point) when the number of GCN layers is increased. In addition, GCNs always work on superpixel-level nodes to reduce computational cost, so pixel-level features cannot be well captured. To deal with these problems, a novel two-branch deeper GCN (TBDGCN) is proposed to combine the advantages of superpixel-based GCN and pixel-based CNN, which can simultaneously extract superpixel-level and pixel-level features of HSIs. In the GCN branch, a GCN module with the DropEdge technique and residual connection is designed to alleviate over-smoothing and over-fitting problem, which results in a deeper network structure with more than ten layers. In the CNN branch, to capture spatial positional information and channel information, a mixed attention mechanism is constructed to extract attention-based spectral-spatial features. The features of the GCN and CNN branches are then fused for classification. Experimental results on three benchmark HSI data sets show that the classification performance of our TBDGCN is better than existing GCN models especially in the case of small sample size.

Recent studies have shown that deep domain adaptation (DA) techniques have good performance on cross-domain hyperspectral image (HSI) classification problems. However, most existing deep HSI DA approaches directly use deep networks to extract features from the data, which ignores the detailed information of HSI in spectral and spatial dimensions. To effectively exploit the spectral–spatial joint information for DA of HSIs, we propose a two-branch attention adversarial DA (TAADA) network in this article. In the TAADA network, a two-branch feature extraction (TBFE) subnetwork is first designed as a generator to extract the attention-based spectral–spatial features. Then, a discriminator based on two classifiers with the multilayer FC-BN-ReLU-Dropout structure is constructed. Based on adversarial learning between the generator and the discriminator, the ability of discriminative feature extraction and cross-domain classification is improved simultaneously. Finally, the TAADA network can adjust the distribution between the source and target domains and extract domain-invariant features. Experimental results on three cross-scene HSI classification tasks show that our proposed TAADA outperforms some existing DA methods.

By means of a sparse collaborative representation mechanism, sparse-representation-based classifiers show a superior performance in hyperspectral image (HSI) classification. Exploiting the similarity and distinctiveness of HSI neighboring pixels, we propose a new nearest regularized joint sparse representation (NRJSR) classification method in this letter. In the classification process of the central test pixel, the weights of different neighboring pixels and the sparse representation coefficients of different training samples are optimized simultaneously within a regularized sparsity model, which can obtain adaptive weights with good joint sparse representation ability. An alternative iteration strategy is used to solve the regularized joint sparsity model. The proposed NRJSR algorithm is tested on two benchmark HSI data sets. Experimental results demonstrate that the proposed algorithm performs better than other sparsity-based algorithms and spectral and spectral-spatial support vector machine classifiers.