TY - GEN
T1 - Wavefusion
T2 - 2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022
AU - Xing, Yinghui
AU - Zhang, Yan
AU - Zhang, Yanning
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Pan-sharpening refers to obtain a high-resolution multispectral (HRMS) image by fusing a panchromatic (PAN) image and a low-resolution multispectral (LRMS) image. Recently, convolutional neural networks (CNNs) have achieved great success in pan-sharpening. However, the down-sampling operations in commonly used CNN-based models lead to information loss, and the corresponding up-sampling operations usually introduce some undesirable artifacts, resulting in suboptimal fusion results. In this paper, we propose a simple but effective wavelet assistant fusion model (WaveFusion) to address aforementioned issue. The proposed model consists of three parts, namely a wavelet feature extraction (WFE) part, a wavelet feature fusion (WFF) part and a reconstruction part. With the assistance of the wavelet transform and also a simple alignment operation, WaveFusion obtains the best fusion result compared with some state-of-the-art methods, especially for the fusion at the full resolution.
AB - Pan-sharpening refers to obtain a high-resolution multispectral (HRMS) image by fusing a panchromatic (PAN) image and a low-resolution multispectral (LRMS) image. Recently, convolutional neural networks (CNNs) have achieved great success in pan-sharpening. However, the down-sampling operations in commonly used CNN-based models lead to information loss, and the corresponding up-sampling operations usually introduce some undesirable artifacts, resulting in suboptimal fusion results. In this paper, we propose a simple but effective wavelet assistant fusion model (WaveFusion) to address aforementioned issue. The proposed model consists of three parts, namely a wavelet feature extraction (WFE) part, a wavelet feature fusion (WFF) part and a reconstruction part. With the assistance of the wavelet transform and also a simple alignment operation, WaveFusion obtains the best fusion result compared with some state-of-the-art methods, especially for the fusion at the full resolution.
KW - convolutional neural network
KW - deep learning
KW - image fusion
KW - Pan-sharpening
KW - wavelet transform
UR - http://www.scopus.com/inward/record.url?scp=85141874854&partnerID=8YFLogxK
U2 - 10.1109/IGARSS46834.2022.9884867
DO - 10.1109/IGARSS46834.2022.9884867
M3 - 会议稿件
AN - SCOPUS:85141874854
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 1083
EP - 1086
BT - IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 17 July 2022 through 22 July 2022
ER -