Heterogeneous remote sensing image change detection based on hybrid network

In order to more quickly and accurately perform the change detection task of heterogeneous remote sensing images, this paper presents a heterogeneous remote sensing image change detection algorithm based on a hybrid network. The algorithm uses a pseudo-siamese network to extract change features between the heterogeneous image blocks in spatial dimension, and uses an early fusion network to extract change features between the heterogeneous image blocks in spectral dimension. The features extracted from the two networks are fused and the fused features are input to the sigmoid layer for binary classification to determine whether the feature has changed. In addition, the contrast loss function is added to the pseudo-siamese network, so that in the features space, the spatial features of the unchanged image pair are closer, and the spatial features of the changed image pair are farther away, which is conducive to improving the network's distinction ability and convergence speed. © 2021, Editorial Board of JBUAA. All right reserved.