Vision Transformer (ViT), known for capturing non-local features, is an effective tool for hyperspectral image classification (HSIC). However, ViT’s multi-head self-attention (MHSA) mechanism often struggles to balance local details and long-range relationships for complex high-dimensional data, leading to a loss in spectral-spatial information representation. To address this issue, we propose a deformable convolution-enhanced hierarchical Transformer with spectral-spatial cluster attention (SClusterFormer) for HSIC. The model incorporates a unique cluster attention mechanism that utilizes spectral angle similarity and Euclidean distance metrics to enhance the representation of fine-grained homogenous local details and improve discrimination of non-local structures in 3-D HSI and 2-D morphological data, respectively. Additionally, a dual-branch multiscale deformable convolution framework augmented with frequency-based spectral attention is designed to capture both the discrepancy patterns in high-frequency and overall trend of the spectral profile in low-frequency. Finally, we utilize a cross-feature pixel-level fusion module for collaborative cross-learning and fusion of the results from the dual-branch framework. Comprehensive experiments conducted on multiple HSIC datasets validate the superiority of our proposed SClusterFormer model, which outperforms existing methods. The source code of SClusterFormer is available at https://github.com/Fang666666/HSIC SClusterFormer.

Hyperspectral image (HSI) classification refers to accurately corresponding each pixel in an HSI to a land-cover label. Recently, the successful application of multiscale and multifeature methods has greatly improved the performance of HSI classification due to their enhanced utilization of the available spectral–spatial information. However, as the number of scales and the number of features increases, it becomes more difficult to achieve an optimal degree of fusion for multiple classifiers [e.g., kernel extreme learning machine (KELM)]. On the other hand, a limited sample size of the HSI may cause overfitting problems, which seriously affects the classification accuracy. Therefore, in this article, a novel multi-structure KELM with attention fusion strategy (MSAF-KELM) is proposed to achieve accurate fusion of multiple classifiers for effective HSI classification with ultrasmall sample rates. First, a multi-structure network is built, which combines multiple scales and multiple features to extract abundant spectral–spatial information. Second, a fast and efficient KELM is employed to enable rapid classification. Finally, a weighted self-attention fusion strategy (WSAFS) is introduced, which combines the output weights of each KELM subbranch and the self-attention mechanism to achieve an efficient fusion result on multi-structure networks. We conducted experiments on four types of HSI datasets with different evaluation methods and compared them with several classical and state-of-the-art methods, which demonstrate the excellent performance of our method on ultrasmall sample rates. The code is available at https://github.com/Fang666666/MSAF-KELM for reproducibility.

Hyperspectral image classification (HSIC) has garnered increasing attention among researchers. While classical networks like convolution neural networks (CNNs) have achieved satisfactory results with the advent of deep learning, they are confined to processing local information. Vision transformers, despite being effective at establishing long-distance dependencies, face challenges in extracting high-representation features for high-dimensional images. In this paper, we present the multiscale efficient attention with enhanced feature transformer (MEA-EFFormer), which is designed for the efficient extraction of spectral–spatial features, leading to effective classification. MEA-EFFormer employs a multiscale efficient attention feature extraction module to initially extract 3D convolution features and applies effective channel attention to refine spectral information. Following this, 2D convolution features are extracted and integrated with local binary pattern (LBP) spatial information to augment their representation. Then, the processed features are fed into a spectral–spatial enhancement attention (SSEA) module that facilitates interactive enhancement of spectral–spatial information across the three dimensions. Finally, these features undergo classification through a transformer encoder. We evaluate MEA-EFFormer against several state-of-the-art methods on three datasets and demonstrate its outstanding HSIC performance.

By using a elastic mechanics model the transverse magnetoelectric voltage coefficient of magnetostrictive-piezoelectric bilayer is derived according to the constitutive equations. The transverse magnetoelectric coupling of nickel zinc ferrite-lead zirconate titanate (Ni_0.8Zn_0.2Fe₂O₄–Pb (Zr,Ti)O₃, NZFO-PZT) layered composites were calculated by using the corresponding material parameters of individual phases. NZFO samples have been synthesized with sol–gel technique. Layered composites NZFO-PZT and NZFO-PZT-NZFO have been fabricated by binding discs of NZFO and commercially available PZT, and the transverse magnetoelectric effect have been investigated. The peak value of transverse magnetoelectric voltage coefficient for NZFO-PZT-NZFO trilayer reaches 252.4 mV/cmOe under a bias magnetic field of about 320 Oe, which is about three times as large as that of NZFO-PZT bilayer. The interface coupling parameter of trilayer is significantly higher than that of bilayer.