Research on Lightweight Remote Sensing Image Semantic Segmentation Method Based on Multilayer Perceptron
Depth semantic segmentation is one of the common remote sensing image applications.The existing semantic segmentation algo-rithms based on depth convolution neural networks can not be effectively applied to image segmentation tasks in real environments.Such net-work models have many parameters,complex calculation and slow operation.For this reason,this paper proposes an image segmentation net-work based on convolutional neural network and multilayer perceptron(MLP),which includes a convolution stage and a MLP stage.An atten-tion control mechanism is added in the process of the jump connection between the encoder and the decoder,so that the network will place more weight in places worthy of attention.The shift based MLP network proposed in this paper can effectively extract local features of images.At the same time,compared with other complex neural network models,the proposed method can effectively reduce the number of parameters and computational complexity,while maintaining the accuracy of segmentation.Finally,the method in this paper is tested on several remote sensing data sets.The results show that the parameters of the model in this paper are 1.471 93M,the average training time is 47.973 218 55s,and the computational complexity is 5.7 GFLOPs,compared with the UNet,UNet++,and SegNet models,which reduces the complexity and running time of the model to a certain extent.
国家科技期刊平台
NSTL
万方数据
维普
