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.