Skip to main content
SpringerLink
Log in

Landsat-8 Image Restoration Based on Kernel Density Regression

  • Conference paper
  • First Online:
Wireless and Satellite Systems (WiSATS 2019)

Included in the following conference series:

  • 1104 Accesses

Abstract

A multi-temporal kernel density regression (KDR) method is proposed in this paper for reflectance restoration. Kernel density regression perform optimization to search the best regression coefficients. The proposed method is applied on the Landsat-8 dataset, and shows a better estimation of the true pixel value from the contaminated images.

This work was supported by the National Natural Science Foundation of China under Grant 61401077 and the China Postdoctoral Science Foundation under Grant 2015M580784.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Zhu, Z., Woodcock, C.E.: Object-based cloud and cloud shadow detection in Landsat imagery. Remote Sens. Environ. 118, 83–94 (2012)

    Article  Google Scholar 

  2. Li, M., Liew, S., Kwoh, L.: Automated production of cloud-free and cloud shadow–free image mosaics from cloudy satellite imagery. In: Proceedings of the ISPRS Congress, Istanbul, 12–13 July 2004 (2004)

    Google Scholar 

  3. Hagolle, O., Huc, M., Pascual, D.V., Dedieu, G.: A multi-temporal method for cloud detection, applied to FORMOSAT-2, VENuS, LANDSAT and SENTINEL-2 images. Remote Sens. Environ. 114(8), 1747–1755 (2010)

    Article  Google Scholar 

  4. Han, Y., Kim, B., Kim, Y., Lee, W.: Automatic cloud detection for high spatial resolution multi-temporal images. Remote Sens. Lett. 5(7), 601–608 (2014)

    Article  Google Scholar 

  5. Qian, J., Luo, Y., Wang, Y.: Cloud detection of optical remote sensing image time series using mean shift algorithm. In: IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 560–562 (2016)

    Google Scholar 

  6. Epanechnikov, V.A.: Non-parametric estimation of a multivariate probability density. Theory Probab. Appl. 14, 153–158 (1969)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Yingcai Honors College, UESTC, Chengdu, 611731, Sichuan, China

    Yuchen Li

  2. School of Environment and Resources, UESTC, Chengdu, 611731, Sichuan, China

    Jiang Qian & Yong Wang

  3. School of Information and Communication Engineering, UESTC, Chengdu, 611731, Sichuan, China

    Jiang Qian & Xiaobo Yang

  4. Department of Geography, Planning, and Environment, East Carolina University, Greenville, NC, 27858, USA

    Yong Wang

  5. Institute of Remote Sensing Big Data, Big Data Research Center of UESTC, Chengdu, 611731, Sichuan, China

    Yong Wang

  6. College of Information Science and Technology, Chengdu University of Technology, Chengdu, China

    Bin Duo

Authors
  1. Yuchen Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jiang Qian
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yong Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiaobo Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Bin Duo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jiang Qian .

Editor information

Editors and Affiliations

  1. Harbin Institute of Technology, Harbin, China

    Min Jia

  2. Harbin Institute of Technology, Harbin, China

    Qing Guo

  3. Harbin Institute of Technology, Harbin, China

    Weixiao Meng

Rights and permissions

Reprints and permissions

Copyright information

© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Li, Y., Qian, J., Wang, Y., Yang, X., Duo, B. (2019). Landsat-8 Image Restoration Based on Kernel Density Regression. In: Jia, M., Guo, Q., Meng, W. (eds) Wireless and Satellite Systems. WiSATS 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 281. Springer, Cham. https://doi.org/10.1007/978-3-030-19156-6_39

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-19156-6_39

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-19155-9

  • Online ISBN: 978-3-030-19156-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation