Unsupervised Hyperspectral Image Change Detection via Deep Learning Self-Generated Credible Labels

Change detection (CD) aims to identify differences in scenes observed at different times. Hyperspectral image (HSI) is preferred for the understanding of land surface changes, since it can provide essential and unique features for CD. However, due to the high-dimensionality and limited data, the HSI-CD task is challenged. While model-driven CD methods are hard to achieve high accuracy due to the weak detection performance for fine changes, data-driven CD methods are hard to be generalized due to the limited data sets. The state-of-art method is to combine a single model-driven method with a data-driven convolutional neural network (CNN). Wherein the pseudo-labels can be generated automatically by the model-driven method and then fed to CNN for training. However, the final detection accuracy is limited by the model-driven method which produces pseudo-labels with one-sidedness and low accuracy. Therefore, the generation of credible pseudo-labels is anticipated and crucial for such a combination. Herein, a novel strategy, the combination of two complementary model-driven methods, structural similarity (SSIM) and change vector analysis (CVA), is proposed to generate credible labels for training a subsequent CNN. The results show that the final accuracy is higher than that of SSIM and CVA. The main contributions of this paper are threefold: 1) a new paradigm for generating credible labels is proposed. 2) SSIM is used for the first time for HSI-CD tasks. 3) an unsupervised end-to-end framework is presented for the HSI-CD. Experimental results demonstrate the effectiveness of the proposed framework.