基于3D多尺度特征融合残差网络的高光谱图像分类

Hyperspectral Image Classification Based on 3D Multi-scale Feature Fusion Residual Network

深度学习中用于训练的高光谱图像(HSI)数据十分有限,因此较深的网络不利于空谱特征的提取.为了缓解该问题,文中提出3D多尺度特征融合残差网络,利用深度学习和多尺度特征融合的方式对光谱-空间特征进行有序的学习.首先对3D-HSI数据进行自适应降维,将降维后的图像作为网络输入.然后,通过多尺度特征融合残差块依次提取光谱-空间特征,融合不同尺度的特征,通过特征共享增强信息流,获得更丰富的特征.最后以端到端的方式训练网络.在相关数据集上的测试表明,文中网络具有良好的分类性能.

Hyperspectral image(HSI)data used for training in deep learning are insufficient,and therefore deeper network is unfavorable for spectral-spatial feature extraction.To solve this problem,a 3D multi-scale feature fusion residual network is proposed.Spectral-spatial features are learned by deep learning and multi-scale feature fusion.Firstly,the dimension of 3D-HSI data is adaptively reduced,and the images after dimensionality reduction are used as the input of the network.Secondly,spectral-spatial features are extracted successively through multi-scale feature fusion residual blocks and features of different scales are fused.The information flow is enhanced through sharing features and richer features are obtained.Finally,the network is trained end-to-end and tested on corresponding datasets.Experimental results show the satisfactory classification performance of the proposed network.