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.

Hyperspectral image (HSI) change detection (CD) plays a crucial role in remote sensing observation. It leverages the abundant spectral and spatial information in bi-temporal HSIs to identify subtle Earth surface changes. Most current deep-learning-based HSI CD methods primarily utilize convolutional neural networks or transformers to extract features from bi-temporal images. However, these methods lack an effective attention mechanism to enhance differential features. In addition, they do not fully leverage the aggregation relationship between the features of bi-temporal images to extract interaction features. To address these challenges, we propose a novel adaptive information weighting and synchronized enhancement network (AIWSEN) for HSI CD. This network employs the information entropy to capture change features specific to the CD task and enhances bi-temporal interaction features. Specifically, an adaptive information weighting attention module (AIWAM) leverages the maximum discrete entropy theorem to capture the difference information. A dual-time synchronic change enhancing module (DSCEM) is designed to extract features by interactively aggregating features from bi-temporal HSIs to enhance difference features. A bi-temporal image feature selection and fusion module (BFSFM) is constructed to filter out important features using forget and update gates. Experimental results on three HSI CD datasets demonstrate that the proposed AIWSEN method outperforms several state-of-the-art methods. The source code of the proposed AIWSEN will be released athttps://github.com/creativeXin/AIWSEN.

In recent years, marine oil spills have occurred frequently, causing serious damage to the marine ecological environment. Hyperspectral images (HSIs) can provide rich spectral and spatial information, and have broad development prospects in marine oil spill detection. This article proposes a hyperspectral marine oil spill detection network, HMOSDN, that integrates improved superpixel segmentation and a mixed attention mechanism (MAM). First, to deal with the extensive clutter and diverse morphology of marine oil spill areas in HSIs, we propose a new superpixel segmentation algorithm based on improved simple linear iterative clustering (ISLIC), which achieves preliminary extraction of spatial features and reduces spatial noise via a Gaussian filter and a pixel intensity smoothing technique (PIST). Then, to further fuse spectral and spatial features and strengthen the feature mining and utilization of fused information, we design a spectral-spatial feature extraction network with an MAM, MAM-SSFEN, which adds a spectral attention module and a spatial attention module, further improving the performance of the deep feature extraction network for oil spill detection. Experiments on the hyperspectral oil spill database (HOSD) demonstrate that our proposed method, HMOSDN, outperforms several other detection techniques regarding area under the curve (AUC) and recall evaluation metrics.

Hyperspectral image classification (HIC) has attracted considerable attention in the last two decades, and significant progress has been made. However, the small sample size problem of HIC is still challenging. This letter presents a two-stage HIC approach that achieves high classification accuracies with few labeled samples. For a given hyperspectral image, the spatial features are first extracted by local binary patterns (LBP). Spatial features and spectral features for each pixel are then stacked into feature vectors. These vectors are fed into SVM to finish the first classification stage. Based on the preliminary classification results, a superpixel segmentation method is introduced for selecting some superpixels which include training samples and all test pixels assigned to some class. These selected superpixels with their labels obtained by SVM are then added to training samples. According to the enlarged training sample set, Random Multi-Graph (RMG) is finally utilized to classify the remaining samples. Experimental results on three benchmark HSI datasets demonstrate that the proposed LBP and RMG-based two-stage method (LBP-RMG2) significantly outperforms several state-of-the-art algorithms with a few labeled samples.

Graph convolutional networks (GCNs) have attracted significant attention in hyperspectral image (HSI) change detection (CD) due to their capability to perform shape-adaptive convolutions and capture complex patterns within HSIs. Existing GCN-based methods typically preprocess bitemporal HSIs into graphs using a specific superpixel segmentation. However, this preprocessing step limits the modeling of spatial topologies to a fixed scale. Besides, these methods do not consider distribution shifts between bitemporal HSIs. To overcome these limitations, this article proposes a fine–coarse–fine progressive graph framework with distribution alignment (FCFDA) to learn progressive features across multilevel graphs for HSI-CD. Specifically, for each bitemporal HSI, we generate multiple hierarchical segmentations ranging from fine to coarse by gradually merging neighboring superpixels and subsequently transforming these segmentations into multilevel graphs. Second, instead of simply concatenating features from different hierarchies, FCFDA integrates them progressively from fine to coarse and then back to fine, generating subtle features tailored to the pixel-wise CD task. Finally, an effective distribution alignment (DA) method is designed to align the feature space of the bitemporal HSIs, thus mitigating the adverse effects of distribution shifts. Experiments conducted on real HSI-CD datasets demonstrate the effectiveness and superiority of the FCFDA.

Very limited training samples pose significant challenges for hyperspectral image (HSI) classification. To address this issue, small-sample learning methods based on classical machine learning or deep learning offer promising solutions. In this article, a novel two-stage learning-based small-sample classification framework is proposed for HSIs, termed edge-preserving features-based smooth ordering (EPFSO). In the proposed EPFSO, a self-training approach and two screening mechanisms are designed to iteratively learn newly labeled samples from a vast pool of unlabeled samples, thereby enhancing classification accuracies by incorporating these additional samples into the training set. The preprocessing step involves using edge-preserving filters to extract key features and generate low-dimensional feature images. Subsequently, all samples are ordered based on spectral similarity and spatial proximity, resulting in a smooth 1-D signal. In the case of limited labeled samples, a specialized self-training approach based on linear interpolation is utilized to iteratively learn newly labeled samples from unlabeled samples. This process continues until no further labeled samples are introduced, enabling gradual improvement in classification performance. In addition, two screening mechanisms are designed into the self-training process to strike a balance between the reliability and quantity of newly labeled samples. Finally, once a sufficient number of training samples are available, a majority voting mechanism is employed to efficiently classify the remaining samples. Experimental results on three open HSI datasets demonstrate that the proposed EPFSO framework outperforms several state-of-the-art methods, including six deep learning approaches. This validates the attractiveness of using EPFSO to address the challenges associated with limited labeled samples.

The functional principal component analysis (FPCA) method can effectively solve the problems of the high dimensionality of data, large information redundancy, and noise interference in hyperspectral image (HSI) classification. However, this unsupervised FPCA cannot make full use of the label information of training samples or spatial information, so that it is impossible to obtain satisfactory classification results. In this letter, a set of improved FPCA methods for HSI classification are proposed. First, the B-spline basis system is used to establish the functional data fitting model, which can convert discrete spectral information into continuous spectral curves and lay the foundation for functional feature extraction. Second, a supervised FPCA (SFPCA) method is built for extracting more effective functional features by making full use of the label information of training samples. Furthermore, to overcome the lack of training samples, two semisupervised FPCA (SSFPCA) methods are proposed for extracting more discriminative functional features and improving the classification accuracies. Finally, we perform the local mean filtering method on the HSI in order to extract the spatial information for each pixel, and then design spectral-spatial classification frameworks based on improved FPCA. Experiments on the commonly used HSI dataset show that improved FPCA can achieve higher classification accuracies than FPCA, and the proposed functional spectral-spatial classification frameworks can greatly improve classification accuracies.

A functional feature extraction method based on rational function approximation for hyperspectral image (HSI) classification is proposed. In digital imagery, the spectral information of a pixel can be regarded as a 1-D signal. An HSI is composed of these 1-D signals arranged in a certain spatial structure. According to the functional characteristic of hyperspectral data, 1-D signals can be approximated by a linear combination of basis functions. Thus, a joint rational basis function system (JRBFS) based on class adaptivity is here first built for an HSI by adaptive Fourier decomposition (AFD). Second, the functional representations (FRs) and corresponding reconstructed spectral curves are obtained by decomposing the original spectral information in a JRBFS. Furthermore, the functional spectral-spatial features are extracted on the basis of FRs by an edge-preserving filtering method, FR-EPFs. Finally, the functional spectral-spatial features are used for HSI classification by SVM. Experimental results for five commonly used HSI data sets demonstrate the effectiveness and advantages of the proposed method FR-EPFs.

This article proposes a functional data discriminant analysis (FDDA) method for hyperspectral image (HSI) classification. This method analyzes and processes the HSI data from a functional point of view, which is a novel perspective in HSI processing. The classical methods achieve dimensionality reduction by directly eliminating the redundancy of the HSI data. However, the proposed method extracts the functional features by utilizing the redundancy of the HSI data. Functional features can effectively reveal inherent characteristics of the HSI data with the change in the wavelengths. Based on this, a regularized weighted fitting model is first built for converting a spectral vector into a spectral curve. Second, an FDDA method defined in the function field is presented for extracting the functional features of the spectral curves. Finally, a novel spectral-spatial framework is designed for classification tasks of HSI data sets. Experimental results in three commonly used HSI data sets indicate that the proposed method is effective and leads to promising classification results compared with some benchmarking methods. More importantly, the work tries to diversify and develop the existing theory and methods of HSI classification from discrete (vector) data learning methods to continuous (functional) data learning methods.

This paper proposes a spectral-spatial classification algorithm based on principal components (PCs)-based smooth ordering and multiple 1-D interpolation, which can alleviate the general classification problems effectively. Because of the characteristics of hyperspectral image, there always exist easily separable samples (ESSs) and difficultly separable samples (DSSs) in view of the different sets of labeled samples. In this paper, the PC analysis is first used for reducing features and extracting the few first PCs of a hyperspectral image. Then, PC-based smooth ordering is designed for the separation of ESSs and DSSs, and multiple 1-D interpolation is used for the accurate classification of the ESSs. Next, the highly confident samples are selected from the ESSs by the spatial neighborhood information, which are added into the training set for the classification of DSSs. In the case of sufficient training samples, a supervised spectral-spatial method is used for classifying the DSSs by combining the spatial information built with popular extended multiattribute profiles. The proposed algorithm is compared with some state-of-the-art methods on three hyperspectral data sets. The results demonstrate that the presented algorithm achieves much better classification performance in terms of the accuracy and the computation time.