Unsupervised and Self-Supervised Tensor Train for Change Detection in Multitemporal Hyperspectral Images

Abstract

Remote sensing change detection (CD) using multitemporal hyperspectral images (HSIs) provides detailed information on spectral–spatial changes and is useful in a variety of applications such as environmental monitoring, urban planning, and disaster detection. However, the high dimensionality and low spatial resolution of HSIs do not only lead to expensive computation but also bring about inter-class homogeneity and inner-class heterogeneity. Meanwhile, labeled samples are difficult to obtain in reality as field investigation is expensive, which limits the application of supervised CD methods. In this paper, two algorithms for CD based on the tensor train (TT) decomposition are proposed and are called the unsupervised tensor train (UTT) and self-supervised tensor train (STT). TT uses a well-balanced matricization strategy to capture global correlations from tensors and can therefore effectively extract low-rank discriminative features, so the curse of the dimensionality and spectral variability of HSIs can be overcome. In addition, the two proposed methods are based on unsupervised and self-supervised learning, where no manual annotations are needed. Meanwhile, the ket-augmentation (KA) scheme is used to transform the low-order tensor into a high-order tensor while keeping the total number of entries the same. Therefore, high-order features with richer texture can be extracted without increasing computational complexity. Experimental results on four benchmark datasets show that the proposed methods outperformed their tensor counterpart, the tucker decomposition (TD), the higher-order singular value decomposition (HOSVD), and some other state-of-the-art approaches. For the Yancheng dataset, OA and KAPPA of UTT reached as high as 98.11% and 0.9536, respectively, while OA and KAPPA of STT were at 98.20% and 0.9561, respectively.