为充分提取高光谱图像（HSI）的光谱空间信息特征，实现HSI的高精度地物分类，提出端到端的多尺度特征融合残差（MFFI）模块。该模块结合了3D多尺度卷积、特征融合以及残差连接3种手段，实现了HSI多尺度光谱空间特征的联合提取。因模块具有端到端特性，可通过堆叠多个MFFI模块得到具有提取深层特征能力的MFFI网络。该网络在Salinas、Indian Pines和University of Pavia 3个HSI数据集的平均总体准确率为99.73%，平均准确率为99.84%，平均卡帕系数为0.9971。结果表明：MFFI模块可以有效提取不同类型地物数据集的光谱空间特征，并取得良好的分类结果。

In order to fully extract the spectral-spatial features of hyperspectral image (HSI) and to achieve high-precision ground object classification of HSI, an end-to-end multi-scale feature fusion identity (MFFI) block is proposed. This block combines 3D multi-scale convolution, feature fusion and residual connection. Through this block, multi-scale spectral-spatial joint features of HSI can be extracted. Because of the end-to-end feature of the block, the final MFFI network can be obtained by stacking multiple MFFI blocks. The average overall accuracy of 99.73%, average accuracy of 99.84%, and Kappa coefficient of 0.9971 are obtained on three HSI datasets: Salinas, Indian Pines and University of Pavia. The results show that the proposed MFFI block can effectively extract the spectral-spatial features of different types of ground object datasets and achieve satisfactory classification results.