TY - JOUR
T1 - Accurate Registration of Remote Sensing Images Based on Local Optimal Transformation
AU - Wang, Bo
AU - Li, Changqing
AU - Tang, Shi
AU - Zhou, Zhiqiang
AU - Zhao, Hong
N1 - Publisher Copyright:
© 2019 Editorial Department of Journal of Beijing Institute of Technology .
PY - 2019/6/1
Y1 - 2019/6/1
N2 - As the basic work of image stitching and object recognition, image registration played an important part in the image processing field. Much previous work in registration accuracy and real-time performance progressed very slowly, especially in registrating images with line feature. An innovative method for image registration based on lines is proposed, it can effectively improve the accuracy and real-time performance of image registration. The line feature can deal with some registration problems where point feature does not work. Our registration process is divided into two parts. The first part determines the rough registration transformation relation between reference image and test image. Then the similarity degree among different transformation and modified non-maximum suppression (MNMS) algorithms are obtained, which produce local optimal solution to optimize the rough registration transformation. The final optimal registration relation can be obtained from two registration parts according to the match scores. The experimental results show that the proposed method makes a more accurate registration relation and performs better in real-time situation.
AB - As the basic work of image stitching and object recognition, image registration played an important part in the image processing field. Much previous work in registration accuracy and real-time performance progressed very slowly, especially in registrating images with line feature. An innovative method for image registration based on lines is proposed, it can effectively improve the accuracy and real-time performance of image registration. The line feature can deal with some registration problems where point feature does not work. Our registration process is divided into two parts. The first part determines the rough registration transformation relation between reference image and test image. Then the similarity degree among different transformation and modified non-maximum suppression (MNMS) algorithms are obtained, which produce local optimal solution to optimize the rough registration transformation. The final optimal registration relation can be obtained from two registration parts according to the match scores. The experimental results show that the proposed method makes a more accurate registration relation and performs better in real-time situation.
KW - Accurate registration relationship
KW - Initial registration relationship
KW - Local optimal transformation
KW - Modified non-maximum suppression (MNMS) algorithm
KW - Similarity degree
UR - http://www.scopus.com/inward/record.url?scp=85071011636&partnerID=8YFLogxK
U2 - 10.15918/j.jbit1004-0579.17190
DO - 10.15918/j.jbit1004-0579.17190
M3 - Article
AN - SCOPUS:85071011636
SN - 1004-0579
VL - 28
SP - 371
EP - 382
JO - Journal of Beijing Institute of Technology (English Edition)
JF - Journal of Beijing Institute of Technology (English Edition)
IS - 2
ER -