LIGHTWEIGHT LANDSLIDE DETECTION METHOD BASED ON DEPTH SEPARABLE CONVOLUTION AND DOUBLE SELF-ATTENTION MECHANISM

The landslide detection methods using remote sensing images are mostly based on the traditional convolutional neural network model with high depth and complexity. The paper proposes a lightweight method based on Depth Separable Convolution and Double Self-Attention Mechanism (DSC-DSAM) for detecting landslides in remote sensing images. This method aims to reduce storage space and improve detection speed while maintaining accuracy. In our model, it starts with using a lightweight convolutional neural network model. Then, the dual self-attention mechanism is applied to improve the accuracy.The proposed method is compared with other existing classification models, and it is shown to have advantages in memory space and detection speed while maintaining accuracy.