计算机科学
特征提取
特征(语言学)
人工智能
规范化(社会学)
块(置换群论)
图像分辨率
图像融合
融合
模式识别(心理学)
解码方法
遥感
计算机视觉
图像(数学)
算法
数学
哲学
语言学
几何学
社会学
人类学
地质学
作者
Bingze Song,Peng Liu,Liangpei Zhang,Lizhe Wang,Luo Zhang,Guojin He,Lajiao Chen,Jianbo Li
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: 1-16
被引量:16
标识
DOI:10.1109/tgrs.2022.3169916
摘要
Due to the limitation of technology and budget, it is often difficult for sensors of a single remote sensing satellite to have both high temporal resolution and high spatial (HTHS) resolution at the same time. In this paper, we proposed a new Multi-level Feature Fusion with Generative Adversarial Network (MLFF-GAN) for generating fusion HTHS images. MLFF-GAN mainly uses U-net-like architecture and its generator is composed of three stages: feature extraction, feature fusion, and image reconstruction. In feature extraction and reconstruction stage, the generator employs the encoding and decoding structure to extract three groups of multi-level features, which can cope with the huge difference of resolution between high-resolution images and low-resolution images. In the feature fusion stage, Adaptive Instance Normalization (AdaIN) block is designed to learn the global distribution relationship between multi-temporal images, and an attention module (AM) is used to learn the local information weights for the change of small areas. The proposed MLFF-GAN was tested on two Landsat and MODIS datasets. Some state-of-the-art algorithms are comprehensively compared with MLFF-GAN. We also carried on the ablation experiment to test the effectiveness of different sub-module in MLFF-GAN. The experiment results and ablation analysis show the better performances of the proposed method when compared with other methods. The code is available at https://github.com/songbingze/MLFF-GAN.
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