Abstract. Recently, many convolutional neural networks (CNN) have been successfully employed in bitemporal synthetic aperture radar (SAR) image change detection. However, most of the existing networks are too heavy and occupy a large volume of memory for storage and calculation. Motivated by this, we propose a lightweight neural network to reduce the computational and spatial complexity and facilitate the change detection on an edge device. We replace normal convolutional layers with bottleneck layers that keep the same number of channels between input and output. Next, we employ dilated convolutional kernels with a few non-zero entries that reduce the running time in convolutional operators. Comparing with the conventional convolutional neural network, our lightweight neural network will be more efficient with fewer parameters. We verify our light-weighted neural network on four sets of bitemporal SAR images. The experimental results show that the proposed network can obtain better performance than the conventional CNN and has better model generalization, especially on the challenging datasets with complex scenes.

中文翻译：

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用于双时相 SAR 图像变化检测的轻量级卷积神经网络

摘要。最近，许多卷积神经网络（CNN）已成功应用于双时相合成孔径雷达（SAR）图像变化检测。然而，现有的网络大多过于庞大，占用大量内存用于存储和计算。受此启发，我们提出了一种轻量级神经网络，以降低计算和空间复杂性并促进边缘设备上的变化检测。我们用瓶颈层替换普通卷积层，在输入和输出之间保持相同数量的通道。接下来，我们使用具有一些非零条目的扩张卷积核来减少卷积算子的运行时间。与传统的卷积神经网络相比，我们的轻量级神经网络将以更少的参数更高效。我们在四组双时相 SAR 图像上验证了我们的轻量级神经网络。实验结果表明，所提出的网络可以获得比传统CNN更好的性能，并且具有更好的模型泛化能力，尤其是在具有复杂场景的具有挑战性的数据集上。