Airborne hyperspectral systems can provide high-resolution hyperspectral images (HSIs) covering large scenes, enabling fine-grained land-cover classification. However, the most popular patch-based methods are limited by low computational efficiency and broken classification results, which hinders the full utilization of this powerful technology in Earth observation applications. Therefore, in this paper, considering the efficiency requirements for large-scale land-cover classification, a novel patch-free approach based on a Transformer-CNN hybrid (PatchOut) framework is proposed. The proposed PatchOut framework adopts an encoder-decoder architecture, enabling rapid semantic segmentation for HSI classification. For the encoder module, we introduce a computationally efficient reduced Transformer module integrated with convolutional neural network (CNN), to leverage their complementary strengths for long-range and local feature extraction, respectively. A multi-scale spatial-spectral feature fusion (MSSSFF) module is also proposed to amalgamate the characteristics of different levels from the encoder, which enhances the overall feature representation. Then, to address the loss of semantic detail and resolution inherent in multi-level feature extraction, a novel feature reconstruction module (FRM) is applied to recover high-quality semantic features. Finally, a large-scale benchmark dataset, Qingpu-HSI, is presented, comprising airborne HSIs covering 33.91 km2 with 20 land-cover classes. Experiments on the Qingpu-HSI and another public dataset demonstrate the superior accuracy and efficiency of our proposed PatchOut framework, outperforming several well-known patch-free and patch-based methods. The Qingpu HSI dataset, along with the PatchOut framework code will be released at https://github.com/busbyjrj/PatchOut.

中文翻译：

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PatchOut：一种基于 transformer-CNN 混合框架的新型无补丁方法，用于对大规模机载高光谱图像进行精细土地覆盖分类


机载高光谱系统可以提供覆盖大型场景的高分辨率高光谱图像 （HSI），从而实现精细的土地覆被分类。然而，最流行的基于补丁的方法受到计算效率低和分类结果损坏的限制，这阻碍了这项强大的技术在地球观测应用中的充分利用。因此，本文考虑到大规模土地覆被分类的效率要求，提出了一种基于 Transformer-CNN 混合 （PatchOut） 框架的新型无补丁方法。所提出的 PatchOut 框架采用编码器-解码器架构，能够为 HSI 分类实现快速语义分割。对于编码器模块，我们引入了一个与卷积神经网络 （CNN） 集成的计算效率高的 Transformer 模块，以利用它们的互补优势分别进行远程和局部特征提取。该文还提出了一种多尺度时空-谱特征融合 （MSSSFF） 模块，以融合编码器不同级别的特性，从而增强整体特征表示。然后，为了解决多级特征提取中固有的语义细节和分辨率的损失，应用了一种新的特征重建模块 （FRM） 来恢复高质量的语义特征。最后，提出了一个大规模基准数据集 Qingpu-HSI，包括覆盖 33.91 km2 和 20 个土地覆盖类别的机载 HSI。在 Qingpu-HSI 和另一个公共数据集上的实验证明了我们提出的 PatchOut 框架的卓越准确性和效率，优于几种著名的无补丁和基于补丁的方法。 青埔 HSI 数据集以及 PatchOut 框架代码将于 https://github.com/busbyjrj/PatchOut 年发布。