Change Detection in Multi-temporal VHR Images Based on Deep Siamese Multi-scale Convolutional Networks

Understand

Very-high-resolution (VHR) images can provide abundant ground details and spatial geometric information.

• Change detection in multi-temporal VHR images plays a significant role in urban expansion and area internal change analysis.
• Nevertheless, traditional change detection methods can neither take full advantage of spatial context information nor cope with the complex internal heterogeneity of VHR images.
• In this paper, a powerful feature extraction model entitled multi-scale feature convolution unit (MFCU) is adopted for change detection in multi-temporal VHR images.