TY - GEN
T1 - EXPERIMENTAL COMPARISON OF REGISTRATION METHODS FOR MULTISENSOR SAR-OPTICAL DATA
AU - Pinel-Puysségur, Béatrice
AU - Maggiolo, Luca
AU - Roux, Michel
AU - Gasnier, Nicolas
AU - Solarna, David
AU - Moser, Gabriele
AU - Serpico, Sebastiano B.
AU - Tupin, Florence
N1 - Publisher Copyright:
© 2021 IEEE
PY - 2021/1/1
Y1 - 2021/1/1
N2 - Synthetic aperture radar (SAR) and optical satellite image registration is a field that developed in the last decades and gave rise to a great number of approaches. The registration process is composed of several steps: feature definition, feature comparison and optimization of a geometric transformation between the images. Feature definition can be done using simple traditional filtering or more complex deep learning (DL) methods. In this paper, two traditional approaches and a DL approach are compared. One can then wonder if the complexity of DL is worth to address the registration task. The aim of this paper is to quantitatively compare approaches rooted in distinct methodological areas on two common datasets with different resolutions. The comparison suggests that, although more complex, the DL approach is more precise than traditional methods.
AB - Synthetic aperture radar (SAR) and optical satellite image registration is a field that developed in the last decades and gave rise to a great number of approaches. The registration process is composed of several steps: feature definition, feature comparison and optimization of a geometric transformation between the images. Feature definition can be done using simple traditional filtering or more complex deep learning (DL) methods. In this paper, two traditional approaches and a DL approach are compared. One can then wonder if the complexity of DL is worth to address the registration task. The aim of this paper is to quantitatively compare approaches rooted in distinct methodological areas on two common datasets with different resolutions. The comparison suggests that, although more complex, the DL approach is more precise than traditional methods.
KW - Multi-modality registration
KW - Optical imagery
KW - SAR
U2 - 10.1109/IGARSS47720.2021.9553640
DO - 10.1109/IGARSS47720.2021.9553640
M3 - Conference contribution
AN - SCOPUS:85114556028
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 3022
EP - 3025
BT - IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021
Y2 - 12 July 2021 through 16 July 2021
ER -