Multilayer Cascade Screening Strategy for Semi-Supervised Change Detection in Hyperspectral Images

Change detection (CD) is an important application of remote sensing, which provides information about land cover changes on the Earth's surface. Hyperspectral image (HSI) can show more spectral information, which greatly improves the ability of remote sensing to identify change features. The challenge is how to overcome the scarcity of labeled samples and extract the change information of high-dimensional spectra in HSI. To solve the previous problem, a semi-supervised CD with multilayer cascade screening strategy (MCS
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CD) that uses both the spatial information and active learning is proposed to select highly reliable unlabeled samples to increase the training sets. The MCS
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CD method can effectively use unlabeled samples to improve accuracy. Additionally, a subspace CD method based on iterative slow feature analysis is designed to extract the most temporally invariant component from the high-dimensional space. Experimental results on four hyperspectral datasets show that with a small number of labeled samples, the proposed method achieves a much better performance than existing CD methods.