Pansharpening Using Unsupervised Generative Adversarial Networks With Recursive Mixed-Scale Feature Fusion

计算机科学 全色胶片 人工智能 特征(语言学) 多光谱图像 模式识别(心理学) 比例(比率) 特征提取 融合机制 图像分辨率 数据挖掘 融合 哲学 物理 脂质双层融合 量子力学 语言学
作者
Yuanyuan Wu,Yuchun Li,Siling Feng,Mengxing Huang
出处
期刊:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing [Institute of Electrical and Electronics Engineers]
卷期号:16: 3742-3759 被引量:14
标识
DOI:10.1109/jstars.2023.3259014
摘要

Panchromatic sharpening (pansharpening) is an important technology for improving the spatial resolution of multispectral (MS) images. The majority of the models are implemented at the reduced resolution, leading to unfavorable results at the full resolution. Moreover, the complicated relationship between MS and panchromatic (PAN) images is often ignored in detail injection. For the mentioned problems, unsupervised generative adversarial networks with recursive mixed-scale feature fusion for pansharpening (RMFF-UPGAN) are modeled to boost the spatial resolution and preserve the spectral information. RMFF-UPGAN comprises a generator and two U-shaped discriminators. A dual-stream trapezoidal branch is designed in the generator to obtain multiscale information. Further, a recursive mixed-scale feature fusion subnetwork is designed. Perform a prior fusion on the extracted MS and PAN features of the same scale. A mixed-scale fusion is conducted on the prior fusion results of the fine-scale and coarse-scale. The fusion is executed sequentially in the above manner building a recursive mixed-scale fusion structure and finally generating key information. A compensation information mechanism is also designed for the reconstruction of key information to compensate for information. A nonlinear rectification block for the reconstructed information is developed to overcome the distortion induced by neglecting the complicated relationship between MS and PAN images. Two U-shaped discriminators are designed and a new composite loss function is defined. The presented model is validated using two satellite data and the outcomes reveal better than the prevalent approaches regarding both visual assessment and objective indicators.

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