Spatial-spectral multi-order gated aggregation network with bidirectional interactive fusion for hyperspectral image classification

Recently, convolutional neural networks (CNNs) have made significant strides in hyperspectral image classification (HSIC) tasks by contextualizing the convolutional kernels as global as possible. However, as the kernel sizes increase, encoding multi-order feature interactions becomes less efficient. Furthermore, self-attention mechanisms and convolutional operations can only handle global and local features independently, resulting in overly complex or simplified interactions. To overcome these limitations, in this work, we propose a novel HSIC framework called the Spatial-Spectral Multi-order Gated Aggregation Network with Bidirectional Interaction Fusion (SS-MoGAN). The proposed SS-MoGAN method integrates simple yet powerful convolutions and gated aggregations into a compact module, facilitating efficient feature extraction and adaptive contextual processing. Specifically, the spatial aggregation (SpaAg) and spectral aggregation (SpeAg) blocks guide the model to explicitly capture the interactions between low- and high-order features within the spatial and spectral dimensions. The bidirectional interaction fusion (BIF) blocks further integrate structural information through a bidirectional cross-attention mechanism, enhancing the representation of fine-grained details. Extensive experiments on three hyperspectral benchmark datasets demonstrate that the proposed SS-MoGAN method outperforms other state-of-the-art methods in HSIC applications. The source code for this work is available at https://github.com/szq0816/SS-MoGAN_HSIC.