Missing Information Reconstruction of Remote Sensing Data: A Technical Review

By Huanfeng Shen, Xinghua Li, Qing Cheng, Chao Zeng, Gang Yang, Huifang Li, Liangpei Zhang. IEEE Geoscience and Remote Sensing Magazine, 2015

517 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/77a771b7a29b3c26a2dc0bc96868f1291fe7b5e5

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Qiang Zhang
2. Stefania Bonafoni
3. Tongwen Li
4. J. Xiao
5. Xinghua Li
6. Decheng Zhou
7. C. Berger
8. Zhiwei Li

Citations in the corpus

• Hyperspectral Image Denoising Employing a Spatial–Spectral Deep Residual Convolutional Neural Network (2018, methodology): “In our future work, we will investigate more efficient learning structures to remove the mixed noise in HSI data, such as stripe noise, impulse noise, and dead lines [54].”
• Robust Image Completion via Deep Feature Transformations (2019, background): “INTRODUCTION Over the past decades, significant progress has been achieved to develop new deep learning networks that achieve improved solutions for a range of computer vision problems, including image classification [1]–[3], face recognition [1], [4], [5], object detection [6]–[8], semantic segment”
• Point-surface fusion of station measurements and satellite observations for mapping PM 2.5 distribution in China: Methods and assessment (2016, methodology): “Firstly, statistical methods will be introduced into filling the missing AOD data (Li et al., 2015b; Shen et al., 2015; Zeng et al., 2013), because awider coverage of satellite-based AOD could provide more comprehensive information for PM2.”
• Deep learning in environmental remote sensing: Achievements and challenges (2020, background): “Missing information in remote sensing data, which are caused by dead lines, gaps, and cloud cover, are common (Shen et al., 2015).”
• Satellite Remote Sensing of Surface Urban Heat Islands: Progress, Challenges, and Perspectives (2018, methodology): “However, there are challenges of efficiency and accuracy in these methods [211].”
• Missing Data Reconstruction in Remote Sensing Image With a Unified Spatial–Temporal–Spectral Deep Convolutional Neural Network (2018, background): “similar design patterns, which makes it possible to consider the reconstruction result of the simulated dead lines in Terra MODIS as the approximate evaluation approach [1].”
• Cloud removal in Sentinel-2 imagery using a deep residual neural network and SAR-optical data fusion (2020, unknown): “In Shen et al. (2015), a comprehensive review https://doi.org/10.1016/j.isprsjprs.2020.05.013 Received 14 January 2020; Received in revised form 15 May 2020; Accepted 18 May 2020 of traditional techniques in provided.”