@inproceedings{4a6d57d32bb044f0b81358293fb2c429,
title = "Wavefusion: Wavelet Assistant Fusion Model for Pan-Sharpening",
abstract = "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.",
keywords = "convolutional neural network, deep learning, image fusion, Pan-sharpening, wavelet transform",
author = "Yinghui Xing and Yan Zhang and Yanning Zhang",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022 ; Conference date: 17-07-2022 Through 22-07-2022",
year = "2022",
doi = "10.1109/IGARSS46834.2022.9884867",
language = "英语",
series = "International Geoscience and Remote Sensing Symposium (IGARSS)",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1083--1086",
booktitle = "IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium",
}