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.

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.