全色胶片
锐化
计算机科学
多光谱图像
卷积神经网络
人工智能
特征(语言学)
模式识别(心理学)
编码(社会科学)
人工神经网络
遥感
计算机视觉
地理
数学
语言学
统计
哲学
作者
Weisheng Li,Xuesong Liang,Meilin Dong
出处
期刊:Remote Sensing
[MDPI AG]
日期:2021-02-02
卷期号:13 (3): 535-535
被引量:9
摘要
With the rapid development of deep neural networks in the field of remote sensing image fusion, the pan-sharpening method based on convolutional neural networks has achieved remarkable effects. However, because remote sensing images contain complex features, existing methods cannot fully extract spatial features while maintaining spectral quality, resulting in insufficient reconstruction capabilities. To produce high-quality pan-sharpened images, a multiscale perception dense coding convolutional neural network (MDECNN) is proposed. The network is based on dual-stream input, designing multiscale blocks to separately extract the rich spatial information contained in panchromatic (PAN) images, designing feature enhancement blocks and dense coding structures to fully learn the feature mapping relationship, and proposing comprehensive loss constraint expectations. Spectral mapping is used to maintain spectral quality and obtain high-quality fused images. Experiments on different satellite datasets show that this method is superior to the existing methods in both subjective and objective evaluations.
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