  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. MFCU can extract multi-scale
spatial-spectral features in the same layer. Based on the unit two novel deep
siamese convolutional neural networks, called as deep siamese multi-scale
convolutional network (DSMS-CN) and deep siamese multi-scale fully
convolutional network (DSMS-FCN), are designed for unsupervised and supervised
change detection, respectively. For unsupervised change detection, an automatic
pre-classification is implemented to obtain reliable training samples, then
DSMS-CN fits the statistical distribution of changed and unchanged areas from
selected training samples through MFCU modules and deep siamese architecture.
For supervised change detection, the end-to-end deep fully convolutional
network DSMS-FCN is trained in any size of multi-temporal VHR images, and
directly outputs the binary change map. In addition, for the purpose of solving
the inaccurate localization problem, the fully connected conditional random
field (FC-CRF) is combined with DSMS-FCN to refine the results. The
experimental results with challenging data sets confirm that the two proposed
architectures perform better than the state-of-the-art methods.
