全色胶片
锐化
计算机科学
多光谱图像
人工智能
卷积神经网络
人工神经网络
变压器
模式识别(心理学)
计算机视觉
工程类
电气工程
电压
作者
Man Zhou,Xueyang Fu,Jie Huang,Feng Zhang,Aiping Liu,Rujing Wang
出处
期刊:IEEE Transactions on Geoscience and Remote Sensing
[Institute of Electrical and Electronics Engineers]
日期:2022-01-01
卷期号:60: 1-15
被引量:14
标识
DOI:10.1109/tgrs.2021.3137967
摘要
In remote sensing imaging systems, pan-sharpening is an important technique to obtain high-resolution multispectral images from a high-resolution panchromatic image and its corresponding low-resolution multispectral image. Due to the powerful learning capability of convolution neural networks (CNNs), CNN-based methods have dominated this field. However, due to the limitation of the convolution operator, long-range spatial features are often not accurately obtained, thus limiting the overall performance. To this end, we propose a novel and effective method by exploiting a customized transformer architecture and information-lossless invertible neural module for long-range dependencies modeling and effective feature fusion in this article. Specifically, the customized transformer formulates the panchromatic (PAN) and multispectral (MS) features as queries and keys to encourage joint feature learning across two modalities, while the designed invertible neural module enables effective feature fusion to generate the expected pan-sharpened results. To the best of our knowledge, this is the first attempt to introduce a transformer and a invertible neural network into the pan-sharpening field. Extensive experiments over different kinds of satellite datasets demonstrate that our method outperforms state-of-the-art algorithms both visually and quantitatively with fewer parameters and flops. Furthermore, the ablation experiments also prove the effectiveness of the proposed customized long-range transformer and effective invertible neural feature fusion module for pan-sharpening.
科研通智能强力驱动
Strongly Powered by AbleSci AI