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Received：11 August 2020，
Published：07 November 2022
移动端阅览
针对高光谱遥感图像空间分辨率低，标注训练样本困难的问题，本文提出一种基于分离卷积（Separable convolution）与密集连接（Dense connection）的轻量级神经网络SDLN模型。该模型基于DenseNet的思想，同时采用计算量更少的分离卷积代替3D卷积，根据算法提取的光谱信息和空间信息，结合目标及周围像素信息推断其中心像素内容，实现对单像素的分类。基于IP、PU和KSC这3个广泛使用的高光谱数据集进行实验，按照分层抽样的方法，每个类别选取少量样本作为训练集，分类精度分别达到了97.4%、97.6%、99.2%，与SSRN、SVM-RBF、MDGCN、DBDA及pResNet多种先进分类算法对比，分类精度提高且时间成本降低。
With the progress of deep learning
researchers are increasingly paying attention to its application in hyperspectral image classification. Many experiments are conducted to achieve a trade-off between accuracy and efficiency to improve the feature extraction performance of neural networks toward small training sample sets.
This work has proposed a high-speed and high-precision neural network structure based on spatial spectral information. A cascaded neural network for spectral spatial information extraction is constructed by combining the idea of DenseNet and adopting dilated convolutions instead of 3D convolutions as the main calculation method. The whole network structure is divided into four components: spectral information extraction
spectral compression
fusion of spatial and spectral information
and voting solution.
Three convolutional layers are built in the spectral information extraction component. In each layer
1×1×7 convolution kernels are used to extract spectral information and maintain the independence of spatial information. The number of kernels is set to 60. In light of the DenseNet idea
the network outputs of the first and second layers are dimensionally split in spectrum and inputted into the third layer. The outputs of the first
second
and third layers are also dimensionally split and inputted into the spectral compression component.
In the spectral compression component
a 1×1×7 convolution kernel is used with a step size set to three. The spectral dimension is compressed
and the number of parameters of the deeper network is lessened by reducing the size of the feature map.
In the spatial and spectral information fusion component
the goal is to fuse spatial information for the first time with 3×3 receptive fields and integrate the spectral information of the data. Separable convolutions are adopted instead of traditional 3D convolutions
and the 3×3×K convolution kernel is decomposed into a 3×3×1 convolution and a 1×1×K convolution. The value of K is equal to the spectral dimension of the input feature map. Then
40 9×9×1 feature maps are outputted.
Voting means that if the output of most pixels is the same value
then the average value of all values will also be pulled near this certain value. In the voting solution component using parameter-free global average pooling
the 9×9×1 feature maps are voted to obtain 1×1×1 output values. These 40 output values are spliced into the fully connected layer
and the classification results our outputted through Softmax.
A series of experiments were carried out on the Indian Pains and Pavia University and Kennedy Space Center datasets. In the IP data set
the average accuracy reaches 95.0%
the overall accuracy 97.4%
and Kappa 0.97 by training with 5% data sets. In the UP data set
OA
AA
and Kappa reach 97.6%
97.1%
and 0.97
respectively
by training with a 0.5% data set. The overall accuracy in the KSC data set can reach 99.2%. The network has been proven to strong feature extraction and classification ability.
This method effectively improves the classification accuracy of hyperspectral images in the case of small sample sets and studies the effect of training and input data sizes on the classification accuracy. The classification accuracy of the network is improved with the increase in the training or input data. However
redundant information generated by a large amount of training data and excessive input data does not help improve the classification performance.
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