A LIGHTWEIGHT CONVOLUTIONAL NEURAL NETWORK FOR BITEMPORAL IMAGE CHANGE DETECTION

Recently, many convolution neural networks have been successfully employed in bitemporal SAR image change detection. However, most of those networks are too heavy where large memory are necessary for storage and calculation. To reduce the computational and spatial complexity and facilitate the change detection on edge devices, in this paper, we propose a lightweight neural network for bitemporal SAR image change detection. In the proposed network, we replace the regular convolutional layers with bottlenecks, which will not increase the number of channels. Furthermore, we employ dilated convolutional kernels with a few non-zero entries which reduces the FLOPs in convlutional operators. Comparing with traditional neural network, our lightweight neural network will be faster, less FLOPs and parameters. We verify our lightweight neural network on two sets of bitemporal SAR images. The experimental results show that the proposed network can obtain the comparable performance with those heavy-weight neural network.