A hyperspectral change detection method based on active learning strategy

In recent years, deep learning has demonstrated its transformative potential in the field of hyperspectral image (HSI) processing but is notoriously data-hungry. However, wanting to obtain a large number of labels is labor-intensive and time-consuming. To reduce the dependence of the model on the label samples while maintaining high detection accuracy, a hyperspectral image change detection algorithm based on active learning strategy (ALCD) is proposed. First, the active learning strategy is employed to select high-value labeled samples from the test set as additional training data, gradually enhancing the model's detection performance. Second, the self-attention module MOAT is introduced to enable effective interaction of local information during the feature extraction process and enhance the network's feature expression capability. Then, the feature interaction and the mixing block are used to blend the features of the bitemporal images, so that the feature distribution of the bitemporal images is more similar, which is conducive to subsequent feature extraction and classification. Experiments on two HIS datasets show that the proposed method can obtain better change detection results than the four comparison algorithms.