Search Results for author: Qiangqiang Yuan

Found 11 papers, 4 papers with code

Coupling Model-Driven and Data-Driven Methods for Remote Sensing Image Restoration and Fusion

no code implementations13 Aug 2021 Huanfeng Shen, Menghui Jiang, Jie Li, Chenxia Zhou, Qiangqiang Yuan, Liangpei Zhang

In this paper, we systematically investigate the coupling of model-driven and data-driven methods, which has rarely been considered in the remote sensing image restoration and fusion communities.

Image Restoration

Fully Polarimetric SAR and Single-Polarization SAR Image Fusion Network

no code implementations18 Jul 2021 Liupeng Lin, Jie Li, Huanfeng Shen, Lingli Zhao, Qiangqiang Yuan, Xinghua Li

The data fusion technology aims to aggregate the characteristics of different data and obtain products with multiple data advantages.

Hybrid Noise Removal in Hyperspectral Imagery With a Spatial-Spectral Gradient Network

1 code implementation1 Oct 2018 Qiang Zhang, Qiangqiang Yuan, Jie Li, Xin-Xin Liu, Huanfeng Shen, Liangpei Zhang

The existence of hybrid noise in hyperspectral images (HSIs) severely degrades the data quality, reduces the interpretation accuracy of HSIs, and restricts the subsequent HSIs applications.

Denoising

Spatial-Spectral Fusion by Combining Deep Learning and Variation Model

no code implementations4 Sep 2018 Huanfeng Shen, Menghui Jiang, Jie Li, Qiangqiang Yuan, Yanchong Wei, Liangpei Zhang

In the field of spatial-spectral fusion, the model-based method and the deep learning (DL)-based method are state-of-the-art.

Hyperspectral Image Denoising Employing a Spatial-Spectral Deep Residual Convolutional Neural Network

2 code implementations1 Jun 2018 Qiangqiang Yuan, Qiang Zhang, Jie Li, Huanfeng Shen, Liangpei Zhang

Hyperspectral image (HSI) denoising is a crucial preprocessing procedure to improve the performance of the subsequent HSI interpretation and applications.

Hyperspectral Image Denoising Image Denoising

Missing Data Reconstruction in Remote Sensing image with a Unified Spatial-Temporal-Spectral Deep Convolutional Neural Network

1 code implementation23 Feb 2018 Qiang Zhang, Qiangqiang Yuan, Chao Zeng, Xinghua Li, Yancong Wei

Because of the internal malfunction of satellite sensors and poor atmospheric conditions such as thick cloud, the acquired remote sensing data often suffer from missing information, i. e., the data usability is greatly reduced.

Cloud Removal

A Multi-Scale and Multi-Depth Convolutional Neural Network for Remote Sensing Imagery Pan-Sharpening

no code implementations28 Dec 2017 Qiangqiang Yuan, Yancong Wei, Xiangchao Meng, Huanfeng Shen, Liangpei Zhang

Pan-sharpening is a fundamental and significant task in the field of remote sensing imagery processing, in which high-resolution spatial details from panchromatic images are employed to enhance the spatial resolution of multi-spectral (MS) images.

Learning a Dilated Residual Network for SAR Image Despeckling

1 code implementation9 Sep 2017 Qiang Zhang, Qiangqiang Yuan, Jie Li, Zhen Yang, Xiaoshuang Ma

In this paper, to break the limit of the traditional linear models for synthetic aperture radar (SAR) image despeckling, we propose a novel deep learning approach by learning a non-linear end-to-end mapping between the noisy and clean SAR images with a dilated residual network (SAR-DRN).

Sar Image Despeckling

Boosting the accuracy of multi-spectral image pan-sharpening by learning a deep residual network

no code implementations22 May 2017 Yancong Wei, Qiangqiang Yuan, Huanfeng Shen, Liangpei Zhang

In the field of fusing multi-spectral and panchromatic images (Pan-sharpening), the impressive effectiveness of deep neural networks has been recently employed to overcome the drawbacks of traditional linear models and boost the fusing accuracy.

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