###### Abstract  
Hyperspectral image (HSI) classification tasks have been adopted in huge applications of remote sensing recently. With the rise of deep learning development, it becomes crucial to investigate how to exploit spatial-spectral features. The traditional approach is to stack models that can encode spatial-spectral features, coupling sufficient information as much as possible, before the classification model. However, this sequential stacking tends to cause information redundancy. In this paper, a novel network utilizing the channel attention combined discrete cosine transform (DCTransformer) to extract spatial-spectral features has been proposed to address this issue. It consists of a detail spatial feature extractor (DFE) with CNN blocks and a base spectral feature extractor (BFE) utilizing the channel attention mechanism (CAM) with a discrete cosine transform (DCT). Firstly, the DFE can extract detailed context information using a series of layers of a CNN. Further, the BFE captures spectral features using channel attention and stores the wider frequency information by utilizing the DCT. Ultimately, the dynamic fusion mechanism has been adopted to fuse the detail and base features. Comprehensive experiments show that the DCTransformer achieves a state-of-the-art (SOTA) performance in the HSI classification task, compared to other methods on four datasets, the University of Houston (UH), Indian Pines (IP), MUUFL, and Trento datasets. On the UH dataset, the DCTransformer achieves an OA of 94.40%, AA of 94.89%, and kappa of 93.92.  
hyperspectral image (HSI) classification; discrete cosine transform (DCT); channel attention mechanism +
Footnote †: journal: Medical Image Sciences  
3D-CNNs, and [13] proposed a Spectral Hierarchical Network (SHN) that utilizes sequential layers of Conv3D and HetConv2D to extract robust and discriminative features from hyperspectral images (HSIs) in the backbone stage. However, the 3D-CNN approaches cannot extract the long-range information due to the restriction of the convolution kernel size, where the long-range information is crucial for understanding complex spatial patterns and contextual relationships in HSI data. Since the vision transformer (ViT) [14] has been proposed and attracted considerable attention in RS image classification, the various variations of vision transformers have been widely studied and adopted for HSI classification [15; 16]. Recently, researchers have presented a spectral learning model named SpectralFormer [17] to derive spectral adjacency dependencies in HSIs. It utilizes a transformer encoder module, which can effectively present spectral features. Nonetheless, it does not capture dense semantic details or make optimal use of the local spatial information. The channel attention mechanism has been demonstrated to help highlight important spectral bands. Ref. [18] introduces the \"Squeeze-and-Excitation\" (SE) block, which remodels the channel-wise responses using channel attention. Ref. [19] proposes the three-branch network, and the SeNet is deployed in the first branch to increase the classification performance successfully. Ref. [20] introduces a bi-branch attention-assisted network, which utilizes the SE block to obtain the attention weights from multi-scale feature maps. However, the traditional SE block has been demonstrated to only utilize the lowest-frequency information of spectral features due to the global average pooling operation. In summary, existing methods either fail to extract global spatial and spectral information or only utilize the low-frequency information of spectral features. Furthermore, previous methods directly fuse spatial and spectral features, neglecting the need for feature filtering to obtain the necessary information. To address these issues, we propose the DCTransformer. Our DCTransformer is a two-branch model, consisting of a base feature extractor (BFE) and a detail feature extractor (DFE). The BFE branch extracts global spectral features, addressing the lack of global spectral information.