CMAAC: Combining Multiattention and Asymmetric Convolution Global Learning Framework for Hyperspectral Image Classification

Hyperspectral image (HSI) classification methods based on deep learning (DL) techniques have succeeded wildly. However, the high-dimensional nonlinearity, spectral mixing, and difficulty in labeling training samples of HSI still hinder the accuracy of HSI classification. Several patch-free-based methods were proposed for HSI classification and have attracted attention. Nevertheless, recent patch-free methods have focused on low- or high-order interactions, ignoring the abundant middle-order interactions, failing to capture the multiorder interactions in the context of HSI, and difficulty extracting discriminative features when the sample data are imbalanced. In this article, a combining multiattention and asymmetric convolution (CMAAC) global learning framework was proposed for insufficient utilization of spectral–spatial information and imbalanced HSI samples. In CMAAC, the channel convolutional long short-term mechanism and multiorder spatial aggregation block (CLMS), which aim to capture multiorder interactions information and extract more discriminant spectral–spatial features effectively, is proposed. The pyramid-enhanced attention mechanism (PEAM) alleviates the information loss in feature flow, retains more spatial information, and better solves the challenge of scale diversity in different land-cover types. The asymmetric convolutional structure at the end of the model highlights the influence of local key feature points and improves its representation ability. Additionally, the framework introduces a joint loss function (JLF) to reduce misclassified classes caused by the imbalance between well-classified and hard-classified samples. We conducted experiments on four benchmark datasets and compared them with the state-of-the-art approaches, demonstrating the model’s effectiveness and superiority.