TY - GEN
T1 - Optimal Component Substitution and Multi-Resolution Analysis Pansharpening Methods Using a Convolutional Neural Network
AU - Palsson, Frosti
AU - Sveinsson, Johannes R.
AU - Ulfarsson, Magnus O.
N1 - Publisher Copyright: © 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - The fusion of a low spatial resolution multispectral image and a high spatial resolution panchromatic image, i.e., pan-sharpening is an important technique in remote sensing where high resolution imagery is needed. Two of the largest families of such methods are the component substitution (CS) and multi-resolution analysis (MRA) methods. These families of methods can be described by general detail injection schemes which are closely related. In this paper, we propose pansharpening methods which are based on directly implementing these schemes using a convolutional neural network (CNN) such that the mean squared error between the down-sampled fused image and the observed multispectral image is minimized. Using a simulated Pleiades dataset we demonstrate that the proposed approach gives excellent results when compared to other state-of-the-art CS, MRA and CNN methods.
AB - The fusion of a low spatial resolution multispectral image and a high spatial resolution panchromatic image, i.e., pan-sharpening is an important technique in remote sensing where high resolution imagery is needed. Two of the largest families of such methods are the component substitution (CS) and multi-resolution analysis (MRA) methods. These families of methods can be described by general detail injection schemes which are closely related. In this paper, we propose pansharpening methods which are based on directly implementing these schemes using a convolutional neural network (CNN) such that the mean squared error between the down-sampled fused image and the observed multispectral image is minimized. Using a simulated Pleiades dataset we demonstrate that the proposed approach gives excellent results when compared to other state-of-the-art CS, MRA and CNN methods.
KW - Pansharpening
KW - component substitution
KW - convolutional neural network
KW - multi-resolution analysis
UR - https://www.scopus.com/pages/publications/85077682382
U2 - 10.1109/IGARSS.2019.8899299
DO - 10.1109/IGARSS.2019.8899299
M3 - Conference contribution
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 3177
EP - 3180
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 -