A lite-cnn for landslides recognition on remote sensing images via structure pruning

High-Efficient landslide recognition on remote sensing images is of great importance to hazard monitoring. In this paper, we introduce MobileL-K, a light Convolutional neural networks(CNNs) to achieve highly efficient landslide recognition. In this network, the depthwise separable convolutions with a large kernel is borrowed to exploit global features on an image. Moreover, an improved EC-based network pruning method was proposed based on continual masking. We prune the MobileL-K to apply to landslide recognition. The experiment results on a benchmark dataset show that proposed method outperforms other compared methods with smaller model size, less FLOPs and higher running speed on GPU.