为了解决基于深度学习的高光谱图像分类方法对于小样本数据分类精度低的问题,提出了一种基于多尺度残差网络的分类模型。该模型通过在残差模块中加入分支结构,分别构造了基于光谱特征和空间特征的提取模块,实现了空间特征和光谱特征的多尺度提取融合,充分利用了高光谱图像中丰富的空谱信息。此外,所提模型使用了动态学习率、批归一化以及Dropout等来提高计算效率和防止过拟合。实验结果表明,该模型在Indian Pines和Pavia University数据集上分别取得了99.07%和99.96%的总体分类精度,与支持向量机和现有的深度学习方法相比,所提模型有效地提高了针对小样本高光谱图像的分类性能。

To solve the problem of low classification accuracy of hyperspectral image classification method based on deep learning for small-sized samples, a classification model based on multi-scale residual network is proposed. By adding a branch structure into the residual module, the model constructs extraction modules based on spectral features and spatial features, respectively, realizes the multi-scale extraction and fusion of spatial and spectral features, and makes full use of the rich spatial and spectral information in hyperspectral images. In addition, dynamic learning rate, batch normalization, and Dropout are used in the proposed model to improve computation efficiency and prevent overfitting. Experimental results show that the proposed method achieves 99.07% and 99.96% of the overall classification accuracy on the datasets of Indian Pines and Pavia University, respectively. Compared with support vector machines and existing deep learning methods, the proposed model effectively improves the classification performance of small-sized sample hyperspectral images.