Open AccessEdge Detection Method Based on Neural Networks for COMS MI Images
Author(s) -
Jinho Lee,
Eun-Bin Park,
Sun-Hee Woo
Publication year - 2016
Publication title -
journal of astronomy and space sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.273
H-Index - 11
eISSN - 2093-5587
pISSN - 2093-1409
DOI - 10.5140/jass.2016.33.4.313
Subject(s) - sobel operator , computer science , artificial intelligence , computer vision , canny edge detector , landmark , edge detection , artificial neural network , enhanced data rates for gsm evolution , process (computing) , satellite , image (mathematics) , remote sensing , template matching , image processing , geology , aerospace engineering , engineering , operating system
Communication, Ocean And Meteorological Satellite (COMS) Meteorological Imager (MI) images are processed for\udradiometric and geometric correction from raw image data. When intermediate image data are matched and compared with\udreference landmark images in the geometrical correction process, various techniques for edge detection can be applied. It is\udessential to have a precise and correct edged image in this process, since its matching with the reference is directly related\udto the accuracy of the ground station output images. An edge detection method based on neural networks is applied for the\udground processing of MI images for obtaining sharp edges in the correct positions. The simulation results are analyzed and\udcharacterized by comparing them with the results of conventional methods, such as Sobel and Canny filters