Recovering missing pixels for Landsat ETM + SLC-off imagery using multi-temporal regression analysis and a regularization method

By Chao Zeng, Huanfeng Shen, Liangpei Zhang. 2013

296 citations recorded by the source corpus.

Subfields: Ecology, Media Technology

View Paper: https://www.semanticscholar.org/paper/c166c0c1c79ff1ece3aa0a3dda4d03e1ab139846

How researchers see this paper

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

In Ecology

1. Qiangqiang Yuan
2. Xinghua Li
3. Chao Zeng
4. Huanfeng Shen
5. Liangpei Zhang
6. Darius Phiri
7. Justin Morgenroth
8. Qiang Zhang

In Media Technology

1. Huanfeng Shen
2. Liangpei Zhang
3. Qiangqiang Yuan
4. Xinghua Li
5. Chao Zeng
6. Qiang Zhang
7. Yancong Wei
8. B. Cooke

Citations in the corpus

• Deep learning in environmental remote sensing: Achievements and challenges (2020, methodology): “These methods obtained satisfactory recovery results, such as gap filling (Zeng et al., 2013), cloud removal (Cheng et al.”
• Missing Information Reconstruction of Remote Sensing Data: A Technical Review (2015, methodology): “Reconstruction results using successive utilization of spatial and temporal methods [2].”
• Developments in Landsat Land Cover Classification Methods: A Review (2017, background): “However, the malfunction of the Scan Line Corrector (SLC) on the ETM+ sensor makes the application of ETM+ images limited [27,28].”
• Remote sensing of forest pest damage: a review and lessons learned from a Canadian perspective* (2016, methodology): “To further extend the value of the Landsat 7 ETM+ sensor for forest change applications, various gap-filling algorithms have been developed and evaluated (Wulder et al. 2008b; Chen et al. 2011; Zeng et al. 2013).”
• Long-term and fine-scale satellite monitoring of the urban heat island effect by the fusion of multi-temporal and multi-sensor remote sensed data: A 26-year case study of the city of Wuhan in China (2016, unknown): “Anadaptivedetermination procedure for the search window and similar pixels can be found in Zeng et al. (2013).”
• Missing Data Reconstruction in Remote Sensing Image With a Unified Spatial–Temporal–Spectral Deep Convolutional Neural Network (2018, methodology): “For the removal of thick cloud, the proposed method was compared with five algorithms: LLHM [30], mNSPI [46], WLR [32], STWR [35], and AWTC [4].”