Full-Text Search:
Home|Journal Papers|About CNKI|User Service|FAQ|Contact Us|中文
《Electronics Optics & Control》 2018-06
Add to Favorite Get Latest Update

An Algorithm for Recognition of Airport in Remote Sensing Image Based on DCNN Model

ZHANG Zuo-xing;YANG Cheng-liang;ZHU Rui-fei;GAO Fang;YU Ye;ZHONG Xing;Changchun Institute of Optics,Fine Mechanics and Physics,Chinese Academy of Sciences,State Key Laboratory of Applied Optics;University of Chinese Academy of Sciences;Chang Guang Satellite Technology Co.,Ltd,Key Laboratory of Satellite Remote Sensing Application Technology of Jilin Province;  
In order to solve the problems of low locating precision and low recognition rate of the airport identification algorithm in sub-meter high-resolution remote sensing images,a new identification algorithm based on Deep Convolutional Neural Network( DCNN) is proposed. Firstly,the bi-cubic interpolation algorithm is used to down-sample the original single-phase remote sensing images and convert them into grayscale images,and the pre-processed images are obtained by fuzzy enhancement. Secondly,the edge information of the images is detected by using Canny edge detection operator,and the straight line segments are extracted by using probability Hough transform. The linear regions are preliminarily screened and merged by judging whether there are parallel lines. Then,DCNN is used for judging the merged regions to acquire the recognition probability of the corresponding regions. Finally,the airport area is obtained by analyzing the probability values of the candidate regions. Simulation experiments were made to the two kinds of remote sensing images with high resolution,the recognition rate was 100% and the mean locating accuracy was 87.53%,which proved the validity and versatility of the proposed algorithm.
【Fund】: 国家重点研发计划(2016YFB0500904);; 吉林省科技厅重点科技关项目(20170204034SF)
【CateGory Index】: TP183;TP751
Download(CAJ format) Download(PDF format)
CAJViewer7.0 supports all the CNKI file formats; AdobeReader only supports the PDF format.
©2006 Tsinghua Tongfang Knowledge Network Technology Co., Ltd.(Beijing)(TTKN) All rights reserved