多尺度3D胶囊网络高光谱图像分类

为解决有限训练样本下的高光谱遥感图像分类特征提取不充分的问题,该论文提出了多尺度3D胶囊网络方法来助力高光谱图像分类.相比传统的卷积神经网络,所提出的网络具有等变性且输入输出形式都是向量形式的神经元而非卷积神经网络中的标量值,有助于获取物体之间的空间关系及特征之间的相关性,且在有限训练样本下能避免过拟合等问题.该网络通过3种不同尺度的卷积核操作对输入图像进行特征提取来获取不同尺度的特征.然后3个分支分别接不同的3D胶囊网络来获取空谱特征之间的关联.最后将3个分支得到的结果融合在一起,采用局部连接并通过间隔损失函数得到分类结果.实验结果表明,该方法在开源的高光谱遥感数据集上具有很好的泛化性能,且相比其他先进的高光谱遥感图像分类方法具有较高的分类精度.

Multi-scale 3D Capsule Network for Hyperspectral Image Classification

Considering the insufficient feature extraction in hyperspectral remote sensing image classification under limited training samples, a multi-scale 3D capsule network is proposed to improve hyperspectral image classification. Compared with the traditional convolutional neural network, the proposed network is equivariant, and its input and output forms are neurons in the form of vectors rather than scalar values in the convolutional neural network. It is conducive to obtaining the spatial relationship between objects and the correlation between features and can avoid problems such as overfitting under limited training samples. Specifically, the network extracts the features of an input image through the convolution kernel operation on three scales to obtain the features of different scales. Then, the three branches are connected to different 3D capsule networks to obtain the correlation between spatial spectrum features. Finally, the results of the three branches are fused, and the classification results are obtained by the local connection and margin loss function. The experimental results reveal that this method has good generalization performance on the open-source hyperspectral remote sensing data set and has higher classification accuracy than other advanced hyperspectral remote sensing image classification methods.