Hyperspectral image (HSI) classification is a challenging task due to subtle interclass difference and large intraclass variability, especially when the available training samples are scarce. To overcome this barrier, this article proposes a novel deep similarity network (DSN) for HSI classification, which not only ensures enough samples for training but also extracts more discriminative features. Unlike other classification methods, our essential idea is to approach the classification task by learning a new similarity measure of pixel pairs under a two-branch neural network. Specifically, a binary classification dataset with same-class and different-class pixel pairs is first constructed, which can significantly increase the number of training samples. Then, the DSN utilizes two subnetworks to extract deep features from the pixel pairs, and computes the similarity between the extracted deep features by a fusion subnetwork. Finally, the output of the DSN is used to measure the similarity to each class and the similarity determines the class label. To make full use of the spatial information, the extended multiattribute profile is incorporated to the DSN. Moreover, a joint loss function is proposed to enhance the discrimination and alleviate the challenge caused by the spatial variability of spectral signatures. Experiments on real HSI datasets verify the superiority of the DSN over several state-of-the-art methods in HSI classification. For instance, the overall accuracy of the DSN on Houston2013 dataset is 89.07%, which achieves a marked improvement of at least 4.2% over all compared methods like convolutional neural network, deep learning with attribute profiles and so on.

中文翻译：

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学习用于高光谱图像分类的深度相似网络

由于细微的类间差异和较大的类内可变性，高光谱图像 (HSI) 分类是一项具有挑战性的任务，尤其是在可用的训练样本稀缺时。为了克服这个障碍，本文提出了一种新的用于 HSI 分类的深度相似性网络 (DSN)，它不仅可以确保有足够的训练样本，还可以提取更多的判别性特征。与其他分类方法不同，我们的基本思想是通过在两分支神经网络下学习像素对的新相似性度量来处理分类任务。具体来说，首先构建具有同类和异类像素对的二元分类数据集，可以显着增加训练样本的数量。然后，DSN 利用两个子网络从像素对中提取深层特征，并通过融合子网络计算提取的深度特征之间的相似度。最后，DSN 的输出用于衡量每个类的相似度，相似度决定了类标签。为了充分利用空间信息，扩展的多属性配置文件被合并到 DSN 中。此外，还提出了联合损失函数来增强辨别力并减轻光谱特征空间可变性带来的挑战。在真实 HSI 数据集上的实验验证了 DSN 在 HSI 分类中优于几种最先进的方法。例如，DSN 在 Houston2013 数据集上的整体准确率为 89.07%，与卷积神经网络、具有属性配置文件的深度学习等所有比较方法相比，实现了至少 4.2% 的显着提高。