面向高光谱影像分类的网络结构自动搜索方法

Automatic Network Structure Search Method for Hyperspectral Image Classification

针对高光谱影像分类中的深度学习模型设计问题,提出了一种面向高光谱影像分类的网络结构自动搜索方法。该方法首先利用可微分结构搜索技术在源高光谱数据集上进行网络结构搜索,然后采用堆叠单元的形式构建深度网络模型,最后利用目标高光谱影像对模型进行分类性能评估。该方法仅在源高光谱数据集上进行一次网络结构搜索,得到的深度网络模型即可应用于其他目标高光谱影像的分类任务,能够有效提高模型利用率。为了提高自动搜索得到的模型的泛化能力和分类精度,采用多源多分辨率的高光谱影像构建源数据集,并引入部分通道连接操作提高搜索效率。试验表明,该方法能够自动搜索出适合高光谱影像分类任务且具备一定通用性的深度网络模型,该模型能够取得较常规深度学习模型更为优异的分类效果,在University of Pavia、Indian Pines、Salinas和Houston 2018这4个目标高光谱影像上分别取得了98.15%、98.74%、97.30%和74.47%的总体分类精度。

Objectives:The quality of network structure is the key to obtain excellent classification performance.Most of existing deep models are designed by the trial-and-error method.When the same model processes different hyperspectral images(HSIs),different network hyperparameters need to be set artificially to adapt to the data characteristics of different images,greatly increasing the difficulty of network design.Aiming at the problem of deep learning model design in hyperspectral image classification,this paper proposes an automatic network structure search method for HSI classification.Methods:In the proposed method,the differentiable structure search technology is first used to search the network structure on the source HSI data sets,then the deep network model is constructed by stacking cells,and finally the classification performance is evaluated on the target HSIs.It should be pointed out that,the proposed method only conducts one search on the source HSI data sets,and the obtained deep model can be applied to other target HSI classification tasks,which can effectively improve the utilization rate of the model.To improve the generalization ability and classification accuracy of the model,multi-source and multi-resolution HSIs are employed to construct the precollected data set,and the partial channel connection operation is introduced to further improve the search efficiency.Results:Three HSIs inculding Houston,Chikusei and Pavia Center are selected as source data.Four HSIs including University of Pavia,Indian Pines,Salinas and Houston 2018 are selected as the target HSIs.The source data is used to determine the network model and the target HSIs are used to evaluate the classification performance.Experimental results show that the proposed method can automatically search out a universal deep network model suitable for HSI classification task,and this model can achieve better classification performance than the conventional deep learning models.The overall classification accuracies of University of Pavia,Indian Pines,Salinas and Houston 2018 are 98.15%,98.74%,97.30%and 74.47%,respectively.Conclusions:Compared with other deep models,the model in this paper is automatically determined by differentiable architecture search algorithm according to the data characteristics of HSIs,effectively improving the classification accuracy of target HSIs while avoiding complex network design.Extensive experiments verify the applicability and effectiveness of the proposed method in HSI classification.