  Small area change detection from synthetic aperture radar (SAR) is a highly
challenging task. In this paper, a robust unsupervised approach is proposed for
small area change detection from multi-temporal SAR images using deep learning.
First, a multi-scale superpixel reconstruction method is developed to generate
a difference image (DI), which can suppress the speckle noise effectively and
enhance edges by exploiting local, spatially homogeneous information. Second, a
two-stage centre-constrained fuzzy c-means clustering algorithm is proposed to
divide the pixels of the DI into changed, unchanged and intermediate classes
with a parallel clustering strategy. Image patches belonging to the first two
classes are then constructed as pseudo-label training samples, and image
patches of the intermediate class are treated as testing samples. Finally, a
convolutional wavelet neural network (CWNN) is designed and trained to classify
testing samples into changed or unchanged classes, coupled with a deep
convolutional generative adversarial network (DCGAN) to increase the number of
changed class within the pseudo-label training samples. Numerical experiments
on four real SAR datasets demonstrate the validity and robustness of the
proposed approach, achieving up to 99.61% accuracy for small area change
detection.
