  Benefited from the rapid and sustainable development of synthetic aperture
radar (SAR) sensors, change detection from SAR images has received increasing
attentions over the past few years. Existing unsupervised deep learning-based
methods have made great efforts to exploit robust feature representations, but
they consume much time to optimize parameters. Besides, these methods use
clustering to obtain pseudo-labels for training, and the pseudo-labeled samples
often involve errors, which can be considered as "label noise". To address
these issues, we propose a Dual Path Denoising Network (DPDNet) for SAR image
change detection. In particular, we introduce the random label propagation to
clean the label noise involved in preclassification. We also propose the
distinctive patch convolution for feature representation learning to reduce the
time consumption. Specifically, the attention mechanism is used to select
distinctive pixels in the feature maps, and patches around these pixels are
selected as convolution kernels. Consequently, the DPDNet does not require a
great number of training samples for parameter optimization, and its
computational efficiency is greatly enhanced. Extensive experiments have been
conducted on five SAR datasets to verify the proposed DPDNet. The experimental
results demonstrate that our method outperforms several state-of-the-art
methods in change detection results.
