计算机工程与应用2019，Vol.55Issue(7)：207-213,7.DOI:10.3778/j.issn.1002-8331.1806-0024
基于U-Net的高分辨率遥感图像语义分割方法
U-Net Based Semantic Segmentation Method for High Resolution Remote Sensing Image
摘要
Abstract
Image segmentation is an important base-part of remote sensing interpretation. High resolution remote sensing image contains complex object information, but the applications of traditional segmentation methods are greatly limited. The segmentation method, represented by the deep convolution neural network, has made a breakthrough in many fields. Aiming at the problem of high resolution remote sensing image segmentation, this paper proposes a deep convolution neural network based on U-Net, which achieves the end to end pixel level semantic segmentation. It expands the original dataset, trains a binary classification model for every class of objects, and then combines the prediction subgraphs to generate the final semantic segmentation image, which has helped us get 94% training accuracy and 90% test accuracy on the dataset of AI classification and recognition contest of CCF satellite images. The experimental results show that the network not only has good generalization ability but also can be used in practical engineering with high segmentation accuracy.关键词
遥感图像/语义分割/卷积神经网络/U-Net/集成学习Key words
remote sensing image/semantic segmentation/convolutional neural network/U-Net/ensemble learning分类
信息技术与安全科学引用本文复制引用
苏健民,杨岚心,景维鹏..基于U-Net的高分辨率遥感图像语义分割方法[J].计算机工程与应用,2019,55(7):207-213,7.基金项目
东南大学成贤学院2017年大学生实践创新训练计划(No.ycx1709). （No.ycx1709）
