计算机工程2017，Vol.43Issue(10)：216-221,6.DOI:10.3969/j.issn.1000-3428.2017.10.036
基于改进全卷积神经网络的航拍图像语义分类方法
Aerial Image Semantic Classification Method Based on Improved Full Convolution Neural Network
摘要
Abstract
The existing Convolution Neural Networks(CNNs) method cannot semantically identify each pixel,and it is difficult to decompose the different types of images from the pixel level.Therefore,an end-to-end full-convolution depth network is proposed to achieve high-resolution aerial image pixel level semantic segmentation and recognition.Full convolution neural network is used to process the image intensity information and Geographical Information System (GIS) information with independent channel,two channel results are merged at the final layer of full convolution neural network,and each pixel is labeled at fully connected pixel level.The Conditional Random Field (CRF) is used as the post-processing method to smooth the similar region,while preserving the edge information in the image.Experimental results show that the proposed algorithm has higher accuracy and better recognition rate than the traditional visual semantic classification algorithm.关键词
图像分类/语义标注/神经网络/目标检测/条件随机场Key words
image classification/semantic annotation/neural network/target detection/Conditional Random Field (CRF)分类
信息技术与安全科学引用本文复制引用
易盟,隋立春..基于改进全卷积神经网络的航拍图像语义分类方法[J].计算机工程,2017,43(10):216-221,6.基金项目
中国博士后科学基金(2016M590912) （2016M590912）
中央高校基本科研业务费专项资金(310832151097). （310832151097）
