Super-Resolution-Guided Progressive Pansharpening Based on a Deep Convolutional Neural Network

被引:97
|
作者
Cai, Jiajun [1 ]
Huang, Bo [1 ]
机构
[1] Chinese Univ Hong Kong, Dept Geog & Resource Management, Hong Kong, Peoples R China
来源
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING | 2021年 / 59卷 / 06期
关键词
Spatial resolution; Remote sensing; Neural networks; Dictionaries; Transforms; Deep learning; multispectral (MS) image; panchromatic image; pansharpening; super-resolution (SR); PAN-SHARPENING METHOD; IMAGE FUSION; QUALITY;
D O I
10.1109/TGRS.2020.3015878
中图分类号
P3 [地球物理学]; P59 [地球化学];
学科分类号
0708 ; 070902 ;
摘要
Pansharpening and super-resolution (SR) methods share the same target to improve the spatial resolution of images. Based on this similarity, we propose and develop a novel pansharpening algorithm that is guided by a deep SR convolutional neural network. The proposed framework comprises three components: an SR process, a progressive pansharpening process, and a high-pass residual module. Specifically, the SR process extracts inner spatial detail that is present in multispectral images. Then, progressive pansharpening is used as a detailed pansharpening process, and the high-pass residual module helps by directly injecting spatial detail from panchromatic images. The performance of the proposed network has been compared with that of traditional and other deep-learning-based pansharpening algorithms based on QuickBird, WorldView-3, and Landsat-8 data, and the results demonstrate the superiority of our algorithm.
引用
收藏
页码:5206 / 5220
页数:15
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