基于空谱融合网络的高光谱图像分类方法

针对高光谱图像分类中提取的空-谱特征表达能力弱及维数较高的问题，提出一种基于空-谱融合网络（SSF-Net）的高光谱图像分类方法。首先，利用双通道卷积神经网络（Two-CNN）同时提取高光谱图像的光谱和空间特征；其次，使用多模态压缩双线性池化（MCB）将所提取的多模态特征向量的外积投射到低维空间，以此产生空-谱联合特征。该特征融合网络，既可以分析光谱特征和空间特征向量中元素之间的复杂关系，同时也避免对光谱和空间向量直接进行外积计算，造成维数过高、计算困难的问题。最终实验表明，与现有基于神经网络的分类方法相比，所提出的高光谱图像分类算法能够获得更高的像元分类精度，表明该网络所提取的空-谱联合向量对高光谱图像具有更强的特征表达能力。

Hyperspectral image classification method based on spatial-spectral fusion network

Concerning the problem that the extracted spatial-spectral features have the shortcoming of weak representation ability and high dimensionality, a HyperSpectral Image (HSI) classification method based on Spatial-Spectral Fusion Network (SSF-Net) was proposed. Firstly, a Two-channel Convolutional Neural Network (Two-CNN) was used to extract the features of spectral domain and spatial domain for HSI respectively. Secondly, Multimodal Compact Bilinear pooling (MCB) was employed to project the outer product of extracted multimodal features to the low dimensional space for producing jointly spatial-spectral features. The fusion network could not only analyze the complex relationship between elements in spectral and spatial eigenvectors, but also avoid directly computing the outer product of spectral and spatial vectors, resulting in high dimension and high computation times. The experimental results show that, compared with the state-of-the-art neural network based classification methods, the proposed algorithm can obtain higher pixel classification accuracy. It indicates that the spatial-spectral joint vector extracted by the proposed network has stronger representation ability.