IEEE Trans Image Process. 2018 Mar;27(3):1259-1270. doi: 10.1109/TIP.2017.2772836. Epub 2017 Nov 13.
Deep learning has gained popularity in a variety of computer vision tasks. Recently, it has also been successfully applied for hyperspectral image classification tasks. Training deep neural networks, such as a convolutional neural network for classification requires a large number of labeled samples. However, in remote sensing applications, we usually only have a small amount of labeled data for training because they are expensive to collect, although we still have abundant unlabeled data. In this paper, we propose semi-supervised deep learning for hyperspectral image classification-our approach uses limited labeled data and abundant unlabeled data to train a deep neural network. More specifically, we use deep convolutional recurrent neural networks (CRNN) for hyperspectral image classification by treating each hyperspectral pixel as a spectral sequence. In the proposed semi-supervised learning framework, the abundant unlabeled data are utilized with their pseudo labels (cluster labels). We propose to use all the training data together with their pseudo labels to pre-train a deep CRNN, and then fine-tune using the limited available labeled data. Further, to utilize spatial information in the hyperspectral images, we propose a constrained Dirichlet process mixture model (C-DPMM), a non-parametric Bayesian clustering algorithm, for semi-supervised clustering which includes pairwise must-link and cannot-link constraints-this produces high-quality pseudo-labels, resulting in improved initialization of the deep neural network. We also derived a variational inference model for the C-DPMM for efficient inference. Experimental results with real hyperspectral image data sets demonstrate that the proposed semi-supervised method outperforms state-of-the-art supervised and semi-supervised learning methods for hyperspectral classification.
深度学习在各种计算机视觉任务中得到了广泛应用。最近，它也成功地应用于高光谱图像分类任务。训练深度神经网络，如用于分类的卷积神经网络，需要大量标记的样本。然而，在遥感应用中，我们通常只有少量的标记数据用于训练，因为它们的收集成本很高，尽管我们仍然有大量的未标记数据。在本文中，我们提出了用于高光谱图像分类的半监督深度学习——我们的方法使用有限的标记数据和大量的未标记数据来训练深度神经网络。更具体地说，我们使用深度卷积递归神经网络（CRNN）通过将每个高光谱像素视为一个光谱序列来进行高光谱图像分类。在提出的半监督学习框架中，利用丰富的未标记数据及其伪标签（聚类标签）。我们建议使用所有训练数据及其伪标签来预训练深度 CRNN，然后使用有限的可用标记数据进行微调。此外，为了利用高光谱图像中的空间信息，我们提出了一种约束狄利克雷过程混合模型（C-DPMM），一种非参数贝叶斯聚类算法，用于半监督聚类，包括成对必须链接和不能链接约束——这会产生高质量的伪标签，从而改善深度神经网络的初始化。我们还为 C-DPMM 推导出了一个变分推理模型，以实现高效推理。使用真实高光谱图像数据集的实验结果表明，所提出的半监督方法在高光谱分类方面优于最先进的监督和半监督学习方法。
