论文标题
LSNET：在遥感图像中用于变更检测的极轻的暹罗网络
LSNet: Extremely Light-Weight Siamese Network For Change Detection in Remote Sensing Image
论文作者
论文摘要
暹罗网络正在成为遥感图像（RSI）变化检测的主流。但是，近年来，更复杂的结构，模块和培训过程的发展导致了繁琐的模型，这阻碍了它们在大规模RSI处理中的应用。为此，本文提出了一个极轻的暹罗网络（LSNET），用于RSI变更检测，该网络用深度可分离的非常可分离的卷积代替了标准卷积，并消除了冗余密集连接，仅保留有效的特征流，同时执行暹罗功能融合，从而极大地进行了压缩参数和计算量。与CCD数据集上的第一名模型相比，LSNET的参数和计算量分别大大减少了90.35 \％和91.34 \％\％，精度仅下降了1.5％。
The Siamese network is becoming the mainstream in change detection of remote sensing images (RSI). However, in recent years, the development of more complicated structure, module and training processe has resulted in the cumbersome model, which hampers their application in large-scale RSI processing. To this end, this paper proposes an extremely lightweight Siamese network (LSNet) for RSI change detection, which replaces standard convolution with depthwise separable atrous convolution, and removes redundant dense connections, retaining only valid feature flows while performing Siamese feature fusion, greatly compressing parameters and computation amount. Compared with the first-place model on the CCD dataset, the parameters and the computation amount of LSNet is greatly reduced by 90.35\% and 91.34\% respectively, with only a 1.5\% drops in accuracy.
