Inter-Orbit Change Detection for High-Resolution SAR Imagery Using Conditional Siamese Network

This paper proposes a method of inter-orbit change detection for high-resolution synthetic aperture radar (SAR) imagery using a conditional Siamese network. The proposed method introduces a sub-network with a condition, which is satellite orbit information, into the multitask Siamese network (MS-net). To introduce the conditions effectively, the weights sharing in one fully-connected layer after connecting the subnetwork is canceled. These tricks enable the proposed network to learn the absorption of layover effects depending on the orbit, which improves change detection performance. Experiments were conducted for detecting car changes in a parking lot by using Asnaro-2 images captured from five different orbits. Compared with the conventional MS-net, the proposed model improves AUC-ROC by 0.015 on average and is more robust to input orbit combinations.