In this letter, we proposed a novel deep feature manifold embedding method to improve feature extraction ability of traditional deep learning methods. This method first obtains deep features of hyperspectral image (HSI) from a trained autoencoder. Then, an intrinsic graph and a penalty graph are constructed to discover the discriminant manifold structure of deep features. Finally, the deep features are mapped into a low-dimensional embedding space, in which samples in intraclass manifold are compacted and samples from interclass manifolds are separated. Experiments on Pavia University, Indian Pines and Urban datasets demonstrate that the proposed method effectively improves the classification performance of HSI compared with other state-of-the-art approaches.

在这封信中，我们提出了一种新颖的深度特征流形嵌入方法，以提高传统深度学习方法的特征提取能力。该方法首先从训练有素的自动编码器获得高光谱图像（HSI）的深层特征。然后，构造一个内在图和惩罚图，以发现深层特征的判别流形结构。最后，将深层特征映射到一个低维的嵌入空间，在该空间中，对类内歧管中的样本进行压缩，并分离类间歧管中的样本。在Pavia大学，Indian Pines和Urban数据集上进行的实验表明，与其他最新方法相比，该方法有效地提高了HSI的分类性能。