基于空谱分组卷积密集网络的高光谱图像分类

Hyperspectral image classification based on spatial-spectral group convolution dense network

针对高光谱图像分类在特征提取过程中高分辨率信息丢失,导致分类精度下降的问题,提出一种基于空谱分组卷积密集网络的高光谱图像分类方法。设计光谱-空间三维分组卷积密集模块,对光谱与空间特征进行分步提取,利用分组卷积构造的密集网络能减少数据固有信息冗余,使高分辨率的特征进行重用,避免细节特征信息丢失;设计光谱残差注意力模块,该模块通过结合空-谱特征计算注意力权重,对提取到的光谱特征进行权重重分配,对光谱信息富有的区域进行增强。实验结果表明,相比于若干最优的深度网络方法,所提高光谱图像分类方法具有更好的分类性能。

Aiming at the issues of high-resolution information loss in the process of feature extraction in hyperspectral image classification,which leads to the decline of classification accuracy,a spatial-spectral group-convolution dense network was proposed.The spatial-spectral 3D group-convolution densenet module was exploited to extract the spectral and spatial features step by step.The information redundancy of hyperspectral data was reduced in the feature extraction process,the high-resolution features were reused through dense connection when using the dense network constructed by group-convolution,avoiding the loss of detailed feature information.The spectral residual attention module was designed and employed to calculate the attention weight combined with the spatial-spectral information,and to redistribute the weight of the extracted spectral features to enhance the area with rich spectral information.Experimental results show that the proposed network performs better than the state-of-the-art neural network-based classification methods.