计算机科学
块(置换群论)
小波
图像融合
人工智能
小波变换
融合
编码(集合论)
图像分辨率
计算机视觉
模式识别(心理学)
图像(数学)
遥感
地理
数学
哲学
集合(抽象数据类型)
程序设计语言
语言学
几何学
作者
Xingjian Zhang,Shuang Li,Zhenyu Tan,Xinghua Li
出处
期刊:Isprs Journal of Photogrammetry and Remote Sensing
日期:2024-05-01
卷期号:211: 281-297
标识
DOI:10.1016/j.isprsjprs.2024.04.016
摘要
Spatiotemporal fusion can provide remote sensing images with both high temporal and high spatial resolution for earth observation applications. Most of the state-of-the-art models require three or even five images as input, which may lead to difficulties in practical applications due to bad weather or data missing. In this paper, the enhanced cross-paired wavelet based spatiotemporal fusion networks (ECPW-STFN) are proposed to improve the accuracy and quality of the fusions with fewer remote sensing images as inputs. Wavelet transform is introduced into spatiotemporal image fusion to achieve separate training of the high and low frequency components of the image to better extract features of different levels. In addition, a compound loss function containing wavelet loss is proposed to facilitate the preservation of details. Also, an enhancement module with convolutional block attention is put forward to further refine the prediction quality. Experiments were conducted to compare the proposed ECPW-STFN with five state-of-the-art methods on the public CIA and Daxing datasets. The results show ECPW-STFN is better than GAN-STFM which also requires two images, and not inferior to the methods requiring at least three inputs, even exceeds the optimal MLFF-GAN in some cases, proving its great superiority and potential. The code will be available at https://github.com/lixinghua5540/ECPW-STFN.
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