软件导刊2024，Vol.23Issue(1)：173-181,9.DOI:10.11907/rjdk.231002
基于多层感知机的轻量级遥感影像语义分割方法研究
Research on Lightweight Remote Sensing Image Semantic Segmentation Method Based on Multilayer Perceptron
摘要
Abstract
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.关键词
遥感图像/语义分割/轻量化/多层感知机/深度学习/注意力机制Key words
remote sensing images/semantic segmentation/lightweight/multilayer perceptron/deep learning/attention mechanism分类
信息技术与安全科学引用本文复制引用
吕文琪,马骁,简夜明,向毅..基于多层感知机的轻量级遥感影像语义分割方法研究[J].软件导刊,2024,23(1):173-181,9.基金项目
重庆市教育委员会重大专项(HZ2021015) （HZ2021015）
