In hyperspectral image (HSI) classification, each pixel sample is assigned to a land-cover category. In the recent past, convolutional neural network (CNN)-based HSI classification methods have greatly improved performance due to their superior ability to represent features. However, these methods have limited ability to obtain deep semantic features, and as the layer’s number increases, computational costs rise significantly. The transformer framework can represent high-level semantic features well. In this article, a spectral–spatial feature tokenization transformer (SSFTT) method is proposed to capture spectral–spatial features and high-level semantic features. First, a spectral–spatial feature extraction module is built to extract low-level features. This module is composed of a 3-D convolution layer and a 2-D convolution layer, which are used to extract the shallow spectral and spatial features. Second, a Gaussian weighted feature tokenizer is introduced for features transformation. Third, the transformed features are input into the transformer encoder module for feature representation and learning. Finally, a linear layer is used to identify the first learnable token to obtain the sample label. Using three standard datasets, experimental analysis confirms that the computation time is less than other deep learning methods and the performance of the classification outperforms several current state-of-the-art methods. The code of this work is available at https://github.com/zgr6010/HSI_SSFTT for the sake of reproducibility.

This paper presents a hypserspectral image (HSI) super-resolution method which fuses a low-resolution hyperspectral image (LR-HSI) with a high-resolution multispectral image (HR-MSI) to get high-resolution HSI (HR-HSI). The proposed method first extracts the nonlocal similar patches to form a nonlocal patch tensor (NPT). A novel tensor-tensor product (t–product) based tensor sparse representation is proposed to model the extracted NPTs. Through the tensor sparse representation, both the spectral and spatial similarities between the nonlocal similar patches are well preserved. Then, the relationship between the HR-HSI and LR-HSI is built using t–product which allows us to design a unified objective function to incorporate the nonlocal similarity, tensor dictionary learning, and tensor sparse coding together. Finally, Alternating Direction Method of Multipliers (ADMM) is used to solve the optimization problem. Experimental results on three data sets and one real data set demonstrate that the proposed method substantially outperforms the existing state-of-the-art HSI super-resolution methods.

The joint use of multi-source remote sensing data for earth observation missions has drawn much attention. Although the fusion of several data sources can improve the accuracy of land-cover identification, many technical obstacles exist, such as disparate data structures, irrelevant physical characteristics, and a lack of training data. In this article, a novel dual-branch method, consisting of a hierarchical convolutional neural network (CNN) and a transformer network, is proposed for fusing multi-source heterogeneous information and improving joint classification performance. First, by combining CNN with a transformer, the proposed dual-branch network can significantly capture and learn spectral-spatial features from hyperspectral image data and elevation features from light detection and ranging (LiDAR) data. Then, to fuse these two sets of data features, a cross-token attention fusion encoder is designed in a specialty. The well-designed deep hierarchical architecture takes full advantage of the powerful spatial context information extraction ability of CNN and the strong long-range dependency modeling ability of the transformer network based on self-attention mechanism. Four standard datasets are used in experiments to verify the effectiveness of the approach. The experimental results reveal that the proposed framework can perform noticeably better than state-of-the-art methods. The source code of the proposed method will be available publicly at https://github.com/zgr6010/Fusion_HCT.git.

In recent years, deep learning-based methods have been extensively utilized in remote sensing image scene classification and have achieved remarkable performance. The wide geographical coverage and resolution differences of scene images result in significant within-class diversity and between-class similarity, hindering the further improvement of classification accuracy. Attention-based methods automatically estimate the importance of local regions by learning weight assignments, which effectively enhance the feature extraction capability of the network. However, methods that solely rely on the network to automatically learn weight assignments may introduce biases in the attention calculations. By analyzing the specific contribution of local features to the key components of global semantics, we propose a collaborative dictionary learning network (CDLNet). CDLNet utilizes the collaborative representation method to decompose global features into a set of key semantic vectors to guide the attention learning process of the network. Specifically, we design a semantic summarization module (SSM), which reconstructs global semantic features by optimizing a low-redundancy dictionary. Next, we propose a global semantic attention module (GSAM), which calculates the contribution of local features to the global feature key information based on their correlation with the reconstructed key semantic set. Finally, an attention transfer loss is introduced to further enhance the attention of low-level feature maps. The experimental results on three publicly available datasets demonstrate that CDLNet can effectively improve within-class diversity and between-class similarity by optimizing the attention learning of the network, thereby achieving great promotion in comparison with state-of-the-art methods. The implementation is publicly available at https://github.com/liuofficial/CDLNet.

Anomaly detection plays an important role in remotely sensed hyperspectral image (HSI) processing. Recently, compressive sensing technology has been widely used in hyperspectral imaging. However, the reconstruction from compressive HSI and detection are commonly completed independently, which will reduce the processing's efficiency and accuracy. In this paper, we propose a framework for hyperspectral compressive sensing with anomaly detection which reconstruct the HSI and detect the anomalies simultaneously. In the proposed method, the HSI is composed of the background and anomaly parts in the tensor robust principal component analysis model. To characterize the low-dimensional structure of the background, a novel tensor nuclear norm is used to constrain the background tensor. As the anomaly part is formed by a few anomalous spectra, the anomaly part is assumed to be a tuber-wise sparse tensor. In addition, to enhance the separation of the background and anomaly, we minimize the sum of Mahalanobis distance of the background pixels. Experiments on four HSIs demonstrate that the proposed method outperforms several state-of-the-art methods on both reconstruction and anomaly detection accuracies.

Hyperspectral image (HSI) super-resolution is a hot topic in remote sensing and computer vision. Recently, tensor analysis has been proven to be an efficient technology for HSI image processing. However, the existing tensor-based methods of HSI super-resolution are not able to capture the high-order correlations in HSI. In this article, we propose to learn a high-order coupled tensor ring (TR) representation for HSI super-resolution. The proposed method first tensorizes the HSI to be estimated into a high-order tensor in which multiscale spatial structures and the original spectral structure are represented. Then, a coupled TR representation model is proposed to fuse the low-resolution HSI (LR-HSI) and high-resolution multispectral image (HR-MSI). In the proposed model, some latent core tensors in TR of the LR-HSI and the HR-MSI are shared, and we use the relationship between the spectral core tensors to reconstruct the HSI. In addition, the graph-Laplacian regularization is introduced to the spectral core tensors to preserve the spectral information. To enhance the robustness of the proposed model, Frobenius norm regularizations are introduced to the other core tensors. Experimental results on both synthetic and real data sets show that the proposed method achieves the state-of-the-art super-resolution performance.