Skip to main content
SpringerLink
Log in

Automatic Retrieval of Water Chlorophyll-A Concentration Based on Push Broom Images

  • Conference paper
  • First Online:
Book cover The 8th International Conference on Computer Engineering and Networks (CENet2018) (CENet2018 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 905))

Included in the following conference series:

  • 751 Accesses

Abstract

In order to fill the domestic blank in automatic retrieval of water Chlorophyll-a concentration using push broom images, an automatic retrieval system of Chlorophyll-a concentration based on push broom images was designed and implemented on ENVI redevelopment platform in this paper. An airborne push broom hyper-spectrometer called Pika L with high spectral and spatial resolutions provides hardware support for retrieving more accurate and real-time Chlorophyll-a concentration. According to the characteristics of Pika L images, the automatic retrieval system mainly includes geometric correction and mosaicking, radiometric calibration, atmospheric correction and Chlorophyll-a concentration retrieval. The results show that the automatic processing of Pika L push broom images based on the ENVI redevelopment is a feasible technical solution and it provides technical support for automatic and real-time retrieval of water Chlorophyll-a concentration.

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 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
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. Cao, Y.: Remote Sensing Monitoring Technology and Application of Water Quality in Macrophytic Lake. Donghua University, Shanghai (2016). (in Chinese)

    Google Scholar 

  2. Zhu, M.H.: Data Processing Method Based on Water Quality Monitoring System. Shanghai University, Shanghai (2008). (in Chinese)

    Google Scholar 

  3. Chen, T.: Visual design and implementation of water quality pollution monitoring based on IDL. University of Electronic Science and Technology of China, Chengdu (2010). (in Chinese)

    Google Scholar 

  4. SpectrononPro: Hyperspectral imaging system software manual.http://docs.resonon.com/spectronon/pika_manual/html/

  5. Ji, Y.: Research on Geometric Correction Method of Pushing and Scanning Pavement Images by Carborne CCD Camera Aided by IMU/DGPS. Capital Normal University, Beijing (2008). (in Chinese)

    Google Scholar 

  6. Cheng, Z.G., Zhang, L.: An aerial image mosaic method based on UAV position and attitude information. Acta Geodaetica Cartogr. Sin. 45(6), 698–705 (2016). (in Chinese)

    Google Scholar 

  7. Wang, S.M., Zhang, A.W., Hu, S.X., Sun, W.D., Meng, X.G., Zhao, W.J.: Geometric correction of linear push-broom hyperspectral camera side-scan imaging. Infrared and Laser Engineering 43(2), 579–585 (2014). (in Chinese)

    Google Scholar 

Download references

Acknowledgement

This work is supported by Special Fund for Marine Public Welfare Scientific Research (201505031), National Key Research and Development Program of China (2016YFC1400300), Shandong Natural Science Foundation for Youths (ZR2017QD009), National Natural Science Foundation of China (61701287), and Key Research and Development Plan of Shandong Province (2017GGX10134).

Author information

Authors and Affiliations

  1. Institute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Shandong Provincial Key Laboratory of Marine Monitoring Instrument Equipment Technology, National Engineering and Technological Research Center of Marine Monitoring Equipment, Qingdao, 266061, China

    Yingying Gai, Enxiao Liu & Yan Zhou

  2. Qingdao Leicent Limited Liability Company, Qingdao, 266100, China

    Shengguang Qin

Authors
  1. Yingying Gai
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Enxiao Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yan Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shengguang Qin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yingying Gai .

Editor information

Editors and Affiliations

  1. School of Computing, Edinburgh Napier University, Edinburgh, UK

    Qi Liu

  2. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Mustafa Mısır

  3. School of Computer, Fudan University, Shanghai, China

    Xin Wang

  4. College of Information Science and Technology/College of Cyber Security, Jinan University, Guangzhou, China

    Weiping Liu

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Gai, Y., Liu, E., Zhou, Y., Qin, S. (2020). Automatic Retrieval of Water Chlorophyll-A Concentration Based on Push Broom Images. In: Liu, Q., Mısır, M., Wang, X., Liu, W. (eds) The 8th International Conference on Computer Engineering and Networks (CENet2018). CENet2018 2018. Advances in Intelligent Systems and Computing, vol 905. Springer, Cham. https://doi.org/10.1007/978-3-030-14680-1_73

Download citation

Publish with us

Policies and ethics

Search

Navigation