Deep learning has received increasing attention in recent years and it has been successfully applied for feature extraction (FE) of hyperspectral images. However, most deep learning methods fail to explore the manifold structure in hyperspectral image (HSI). To tackle this issue, a novel graph-based deep learning model, termed deep locality preserving neural network (DLPNet), was proposed in this paper. Traditional deep learning methods use random initialization to initialize network parameters. Different from that, DLPNet initializes each layer of the network by exploring the manifold structure in hyperspectral data. In the stage of network optimization, it designed a deep-manifold learning joint loss function to exploit graph embedding process while measuring the difference between the predictive value and the actual value, then the proposed model can take into account the extraction of deep features and explore the manifold structure of data simultaneously. Experimental results on real-world HSI datasets indicate that the proposed DLPNet performs significantly better than some state-of-the-art methods.

近年来，深度学习受到越来越多的关注，并且已成功地应用于高光谱图像的特征提取（FE）。但是，大多数深度学习方法无法探索高光谱图像（HSI）中的流形结构。为了解决这个问题，本文提出了一种新颖的基于图的深度学习模型，称为深度局部保存神经网络（DLPNet）。传统的深度学习方法使用随机初始化来初始化网络参数。与此不同的是，DLPNet通过探索高光谱数据中的流形结构来初始化网络的每一层。在网络优化阶段，它设计了一个深流形学习关节损失函数，以利用图嵌入过程来测量预测值与实际值之间的差异，然后，提出的模型可以考虑深度特征的提取，并同时探索数据的流形结构。在真实HSI数据集上的实验结果表明，提出的DLPNet的性能明显优于某些最新方法。