TY - GEN
T1 - Effect Analysis in the Fine Co-Registration of Very-High-Resolution Satellite Images for Unsupervised Change Detection
AU - Han, Youkyung
AU - Jung, Sejung
AU - Liu, Sicong
AU - Yeom, Junho
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - Fine co-registration that precisely aligns multiple images acquired over a given area is an important process to exploit the very high resolution (VHR) multitemporal images in a wide range of remote sensing applications. The objective of this study is to analyze the effect of the fine co-registration performance on an unsupervised change detection between VHR images. To this end, we extract registration noise (RN) samples, which are denoted as misaligned pixels in a local region. Then, the location of conjugate points (CPs) is positioned by analyzing the local distribution of the extracted RN samples. The CPs are employed for generating a non-rigid transformation model to warp a sensed image into a reference image. An unsupervised change vector analysis approach is used to validate the effectiveness of the proposed fine co-registration performance. Experiments are implemented on a Worldview-3 VHR multispectral dataset.
AB - Fine co-registration that precisely aligns multiple images acquired over a given area is an important process to exploit the very high resolution (VHR) multitemporal images in a wide range of remote sensing applications. The objective of this study is to analyze the effect of the fine co-registration performance on an unsupervised change detection between VHR images. To this end, we extract registration noise (RN) samples, which are denoted as misaligned pixels in a local region. Then, the location of conjugate points (CPs) is positioned by analyzing the local distribution of the extracted RN samples. The CPs are employed for generating a non-rigid transformation model to warp a sensed image into a reference image. An unsupervised change vector analysis approach is used to validate the effectiveness of the proposed fine co-registration performance. Experiments are implemented on a Worldview-3 VHR multispectral dataset.
KW - Fine co-registration
KW - registration noise
KW - unsupervised change detection
KW - very-high-resolution images
UR - http://www.scopus.com/inward/record.url?scp=85077685966&partnerID=8YFLogxK
U2 - 10.1109/IGARSS.2019.8898916
DO - 10.1109/IGARSS.2019.8898916
M3 - Conference contribution
AN - SCOPUS:85077685966
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 1558
EP - 1561
BT - 2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019
Y2 - 28 July 2019 through 2 August 2019
ER -