SAR remote sensing image change detection method based on local space deep feature

This research offers an image change detection strategy based on convolutional-wavelet neural networks based on Laplace support vector machine (LapSVM) (CWNLSN) to enhance the generalization and robustness of convolutional neural network (CNN) in change detection applications. Firstly, by using the sample labeling method, high confidence "pseudo labels" are obtained, and the network training set, classification training set, and test set are divided. Secondly, discrete wavelet pooling is used to retrieve local space deep characteristics in CNN. Then, design a local space deep feature classification (LSDC) module based on LapSVM to classify the deep features and distinguish the changed information in the test set. Finally, comparative experiments and ablation experiments were conducted on multiple sets of real remote sensing datasets for testing. The results indicate that the proposed method achieved a more significant change detection effect.