LSNet: Extremely Light-Weight Siamese Network For Change Detection in Remote Sensing Image
This work addresses the need for efficient large-scale remote sensing image processing, though it is incremental as it builds on existing Siamese network methods.
The paper tackles the problem of cumbersome models in remote sensing image change detection by proposing LSNet, an extremely lightweight Siamese network that reduces parameters by 90.35% and computation by 91.34% compared to the state-of-the-art, with only a 1.5% drop in accuracy.
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.