Hyperspectral image (HSI) contains abundant spectral-spatial information, which is widely used in many fields. HSI classification is a fundamental and important task, which aims to assign each pixel a specific class label. However, the high spectral variability and the limited labeled samples create challenges for HSI classification, which results in poor data separability and makes it difficult to learn highly discriminative semantic features. In order to address the above problems, a novel spectral-spatial center-aware bottleneck Transformer is proposed. First, the highly relevant spectral information and the complementary spatial information at different scales are integrated to reduce the impact caused by the high spectral variability and enhance the HSI’s separability. Then, the feature correction layer is designed to model the cross-channel interactions, thereby promoting the effective cooperation between different channels to enhance overall feature representation capability. Finally, the center-aware self-attention is constructed to model the spatial long-range interactions and focus more on the neighboring pixels that have relatively consistent spectral-spatial properties with the central pixel. Experimental results on the common datasets show that compared with the state-of-the-art classification methods, S2CABT has the better classification performance and robustness, which achieves a good compromise between the complexity and the performance.

To improve the representation of spectral features and enhance the complementarity of heterogeneous spatial features in the joint classification of hyperspectral image (HSI) and light detection and ranging image (LiDAR), this paper proposes a joint classification network with interactive extraction and heterogeneous balance (JCNIH). On the one hand, this paper designs an interactive method for information exchange between large-span spectral features and small-span spectral features, which improves the adaptability and representation of spectral features. On the other hand, this paper designs a balance loss to constrain the extraction of heterogenous spatial features, which can keep the feature extraction balance and enhance the spatial complementarity. Then, the extracted spectral features and heterogeneous spatial features are aggregated for classification. Finally, comparative and ablation experiments demonstrate the effectiveness of JCNIH, and the experimental results show that the JCNIH outperforms the state-of-the-art methods in overall accuracy by 10.33% on three public datasets.

Hyperspectral image (HSI) classification is a basic and significant task in remote sensing, the aim of which is to assign a class label to each pixel in an image. Recently, deep learning networks have been widely applied in HSI classification. They can extract discriminative spectral–spatial features through spectral weighting and multiscale spatial information modeling. However, existing spectral weighting mechanisms lack the ability to explore the inter-class spectral overlap caused by spectral variability. Moreover, current multiscale fusion strategies ignore semantic conflicts between features with large-scale differences. To address these problems, a class-aware progressive multiscale fusion transformer (CPMFFormer) is proposed. It first introduces class information into a spectral weighting mechanism. This helps CPMFFormer to learn class-specific spectral weights and enhance class-discriminative spectral features. Then, a center residual convolution module is constructed to extract features at different scales. It is embedded with a center feature calibration layer to achieve hierarchical enhancement of representative spatial features. Finally, a progressive multiscale fusion strategy is designed to promote effective collaboration between features at different scales. It achieves a smooth semantic transition by gradually fusing adjacent scale features. Experiments using five public HSI datasets show that CPMFFormer is rational and effective.

Ship detection in range-compressed (RC) synthetic aperture radar (SAR) images requires recognizing spatially extended responses rather than compact target appearances. Because elongated responses are not unique to ships, clutter can interfere with both feature aggregation and candidate selection. We propose FM-DETR, a frequency–morphology collaborative Transformer that extends RC-aware feature modeling to decoder-query initialization. An asymmetric cross-scale feature pyramid uses orthogonal strip convolutions to accommodate the unequal range and azimuth extents of target responses. A Fourier-gated intra-scale interaction module complements spatial self-attention with learnable spectral modulation, while a content-dependent gate regulates the spectral residual. The refined representation provides spatial guidance for multiscale candidate scoring and query selection. Evaluation follows the official RCShip-1.0 split, with synthetic training samples and scene-derived test samples reconstructed in the RC domain. FM-DETR achieves average precision (AP@0.5) of 90.14%, a precision of 89.53%, and a recall of 84.42%. With the backbone and training settings held fixed, the complete model improves AP@0.5 by 4.00 percentage points over the baseline. These results support integrating direction-sensitive aggregation, spectral refinement, and guided query initialization for ship detection in RC images.