Deep learning methods have played a more important role in hyperspectral image classification. However, general deep learning methods mainly take advantage of the samplewise information to formulate the training loss while ignoring the intrinsic data structure of each class. Due to the high spectral dimension and great redundancy between different spectral channels in the hyperspectral image, these former training losses usually cannot work so well for the deep representation of the image. To tackle this problem, this work develops a novel deep manifold embedding method (DMEM) for deep learning in hyperspectral image classification. First, each class in the image is modeled as a specific nonlinear manifold, and the geodesic distance is used to measure the correlation between the samples. Then, based on the hierarchical clustering, the manifold structure of the data can be captured and each nonlinear data manifold can be divided into several subclasses. Finally, considering the distribution of each subclass and the correlation between different subclasses under data manifold, DMEM is constructed as the novel training loss to incorporate the special classwise information in the training process and obtain discriminative representation for the hyperspectral image. Experiments over four real-world hyperspectral image datasets have demonstrated the effectiveness of the proposed method when compared with general sample-based losses and showed superiority when compared with state-of-the-art methods.

中文翻译：

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用于高光谱图像分类的深流形嵌入。

深度学习方法在高光谱图像分类中发挥了更重要的作用。但是，一般的深度学习方法主要利用样本信息来制定训练损失，而忽略每个类的固有数据结构。由于高光谱尺寸以及高光谱图像中不同光谱通道之间的巨大冗余，这些以前的训练损失通常无法很好地用于图像的深层表示。为了解决这个问题，这项工作开发了一种新颖的深度流形嵌入方法（DMEM），用于高光谱图像分类中的深度学习。首先，将图像中的每个类别建模为特定的非线性流形，并使用测地线距离来测量样本之间的相关性。然后，基于分层聚类，数据的流形结构可以被捕获，每个非线性数据流形可以分为几个子类。最后，考虑到数据流形下每个子类的分布以及不同子类之间的相关性，将DMEM构造为一种新颖的训练损失，将特殊的类信息纳入训练过程中，并获得高光谱图像的判别表示。在四个真实世界的高光谱图像数据集上进行的实验证明，与基于常规样本的损失相比，该方法是有效的；与最新方法相比，它具有优越性。考虑到数据流形下每个子类的分布以及不同子类之间的相关性，将DMEM构造为一种新颖的训练损失，将特殊的类信息纳入训练过程中，并获得高光谱图像的判别表示。在四个真实世界的高光谱图像数据集上进行的实验证明，与基于常规样本的损失相比，该方法是有效的；与最新方法相比，它具有优越性。考虑到数据流形下每个子类的分布以及不同子类之间的相关性，将DMEM构造为一种新颖的训练损失，将特殊的类信息纳入训练过程中，并获得高光谱图像的判别表示。在四个真实世界的高光谱图像数据集上进行的实验证明，与基于常规样本的损失相比，该方法是有效的；与最新方法相比，它具有优越性。