Skip to main content
SpringerLink
Log in

Multi-agent hyperspectral and lidar features fusion for urban vegetation mapping

  • RESEARCH
  • Published:
Earth Science Informatics Aims and scope Submit manuscript

Abstract

Urban vegetation recognition based on remote sensing data is highly affected by the complexities of urban areas due to the existence of various kinds of objects and their relations. Spectral similarities between tree canopies and types of grass lands, spatial adjacency between houses and tall trees, shadow and occluded areas make some difficulties for recognition of the plant species. In this research, the capabilities of multi-agent systems are utilized for feature fusion of hyperspectral imagery and lidar data for improving the vegetation recognition results in urban areas. The proposed algorithm has two main steps composed of generating a knowledge base containing spectral and height features extracted from input hyperspectral and Lidar data, respectively, and performing the hierarchical classification process to generate vegetation classification map based on parallel processing by object recognition agents. Evaluation of the capabilities of the proposed methodology is performed on hyperspectral and lidar DSM over Houston University and its surrounding areas. According to the obtained results, fusion of hyperspectral and Lidar DSM with the capabilities of multi-agent processing can improve the overall accuracy of vegetation recognition results for about 15.53% and 6.58% comparing with performing multi-agent and maximum likelihood classifier only on hyperspectral image, respectively.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Data Availability

The data that support the findings of this study are openly available in IEEE GRSS data fusion committee at https://www.grss-ieee.org.

Notes

  1. Light Detection and Ranging Digital Surface Model.

References

  • Cobby DM, Mason DC, Horritt MS, Bates PD (2003) Two-dimensional hydraulic flood modelling using a finite-element mesh decomposed according to vegetation and topographic features derived from airborne scanning laser altimetry. Hydrol Process 17(10):1979–2000

    Article  Google Scholar 

  • Datt B (1999) A new reflectance index for remote sensing of chlorophyll content in higher plants: tests using Eucalyptus leaves. J Plant Physiol 154(1):30–36

    Article  Google Scholar 

  • Debes C, Merentitis A, Heremans R, Hahn J, Frangiadakis N, van Kasteren T, ... & Philips W (2014) Hyperspectral and LiDAR data fusion: outcome of the 2013 GRSS data fusion contest. IEEE J Select Topics Appl Earth Obs Remote Sens 7(6):2405–2418

  • Del Frate F, Pacifici F, Schiavon G, Solimini C (2007) Use of neural networks for automatic classification from high-resolution images. IEEE Trans Geosci Remote Sens 45(4):800–809

    Article  Google Scholar 

  • Fang B, Li Y, Zhang H, Chan JCW (2020) Collaborative learning of lightweight convolutional neural network and deep clustering for hyperspectral image semi-supervised classification with limited training samples. ISPRS J Photogramm Remote Sens 161:164–178

    Article  Google Scholar 

  • Gadal SM, Ouerghemmi W, Barlatier R, Mozgeris G (2019) Critical analysis of urban vegetation mapping by satellite multispectral and airborne hyperspectral imagery. In Proceedings of the 5th international conference on geographical information systems theory, Appl Manag (GISTAM 2019), 97–104. https://doi.org/10.5220/0007721400970104

  • Ghamisi P, Benediktsson JA, Phinn S (2015) Land-cover classification using both hyperspectral and LiDAR data. Int J Image Data Fusion 6(3):189–215

    Article  Google Scholar 

  • Gitelson AA, Gritz Y, Merzlyak MN (2001) Optical properties and non-destructive estimation of anthocyanin content in plant leaves. J Plant Physiol 74(1):38–45

    Google Scholar 

  • Ham J, Chen Y, Crawford MM, Ghosh J (2005) Investigation of the random forest framework for classification of hyperspectral data. IEEE Trans Geosci Remote Sens 43(3):492–501

    Article  Google Scholar 

  • Hasani H, Samadzadegan F, Reinartz P (2017) A metaheuristic feature-level fusion strategy in classification of urban area using hyperspectral imagery and LiDAR data. Eur J Remote Sens 50(1):222-236.44

    Article  Google Scholar 

  • He S, Du H, Zhou G, Li X, Mao F, Zhu D, Xu Y, Zhang M, Huang Z, Liu H, Lou X (2020) Intelligent Mapping of Urban Forests from High-Resolution Remotely Sensed Imagery Using Object-Based U-Net-DenseNet-Coupled Network. Remote Sens 2020(12):3928. https://doi.org/10.3390/rs12233928

    Article  Google Scholar 

  • Huete AR (1988) A soil-adjusted vegetation index (SAVI). Remote Sens Environ 25(3):295–309

    Article  Google Scholar 

  • Liu L, Coops NC, Aven NW, Pang Y (2017) Mapping urban tree species using integrated airborne hyperspectral and LiDAR remote sensing data. Remote Sens Environ 200(2017):170–182

    Article  Google Scholar 

  • Lodha SK, Helmbold DP, Kreps EJ, Fitzpatrick D (2006) Aerial LiDAR data classification using support vector machines (SVM). Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06). https://doi.org/10.1109/3DPVT.2006.23

  • Man Q, Dong P, Guo H (2015) Pixel-and feature-level fusion of hyperspectral and lidar data for urban land-use classification. Int J Remote Sens 36(6):1618–1644

    Article  Google Scholar 

  • Mason DC, Cobby DM, Horritt MS, Bates PD (2003) Floodplain friction parameterization in two-dimensional river flood models using vegetation heights derived from airborne scanning laser altimetry. Hydrol Process 17(9):1711–1732

    Article  Google Scholar 

  • Melgani F, Bruzzone L (2004) Classification of hyperspectral remote sensing images with support vector machines. IEEE Trans Geosci Remote Sens 42(8):1778–1790

    Article  Google Scholar 

  • Merzlyak MN, Gitelson AA, Chivkunova OB, Rakitin VY (1999) Non-destructive optical detection of pigment changes during leaf senescence and fruit ripening. J Physiol Plantarum 106(1):135–141

    Article  Google Scholar 

  • Muller KR, Mika S, Ratsch G, Tsuda K, Scholkopf B (2001) An introduction to kernel-based learning algorithms. IEEE Trans Neural Networks 12(2):181–201

    Article  Google Scholar 

  • Neyns R, Canters F (2022) Mapping of urban vegetation with high-resolution remote sensing: A review”. Remote Sens 2022(14):1031. https://doi.org/10.3390/rs14041031

    Article  Google Scholar 

  • Penuelas J, Filella I, Gamon JA (1995) Assessment of photosynthetic radiation-use efficiency with spectral reflectance. Journal of New Phytologist 131(3):291–296

    Article  Google Scholar 

  • Rottensteiner F, Jansa J (2002) Automatic extraction of buildings from LIDAR data and aerial images. Int Arch Photogram Remote Sens Spat Inform Sci 34(4):569–574

    Google Scholar 

  • Roujean JL, Breon FM (1995) Estimating PAR absorbed by vegetation from bidirectional reflectance measurements. Remote Sens Environ 51(3):375–384

    Article  Google Scholar 

  • Samadzadegan F, Tabib Mahmoudi F, Schenk T (2010) An agent-based method for automatic building recognition from lidar data. Can J Remote Sens 36(3):211–223

    Article  Google Scholar 

  • Sims DA, Gamon J (2002) Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sens Environ 81(2–3):337–354. https://doi.org/10.1016/S0034-4257(02)00010-X

    Article  Google Scholar 

  • Subramanyam BC, Naga Lakshmi G (2014) Hyperspectral Image Classification Using SVM. Int J Eng Comp Sci 3(9):8116–8119

    Google Scholar 

  • Tabib Mahmoudi F, Samadzadegan F, Reinartz P (2013) Object oriented image analysis based on multi-agent recognition system. Comput Geosci 54:219–230

    Article  Google Scholar 

  • Vogelmann J, Rock BN, Moss DM (1993) Red edge spectral measurements from sugar maple leaves. Int J Remote Sens 14(8):1563–1575

  • Wang K, Wang T, Liu X (2019) A review: Individual tree species classification using integrated airborne LiDAR and optical imagery with a focus on the urban environment. Forests 2019(10):1

    Google Scholar 

  • Wang Y, Duan H (2018) Classification of hyperspectral images by SVM using a composite kernel by employing spectral, spatial and hierarchical structure information. Remote Sensing 10(3):441

    Article  Google Scholar 

  • Xu Z, Zhou Y, Wang S, Wang L, Li F, Wang S, Wang Z (2020) A novel intelligent classification method for urban green space based on high-resolution remote sensing images. Journal of Remote Sensing 12(22):3845

    Article  Google Scholar 

  • Yoon T (2008) Object recognition based on multi-agent spatial reasoning [Doctoral dissertation, Ohio State University]. Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1206075792

Download references

Funding

No funding was obtained for this study.

Author information

Authors and Affiliations

  1. MSc of Remote Sensing, Department of Geomatics, Faculty of Civil Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran

    Sahar Khoramak

  2. Department of Geomatics, Faculty of Civil Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran

    Fatemeh Tabib Mahmoudi

Authors
  1. Sahar Khoramak
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fatemeh Tabib Mahmoudi
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

S.Khoramak analyzed and interpreted the obtained results from the proposed multi-agent system. F.Tabib Mahmoudi performed the comparison between the capabilities of the proposed method and other methodologies, and was a major contributor in writing the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Fatemeh Tabib Mahmoudi.

Ethics declarations

Competing interests

The authors declare no competing interests.

Conflict of Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Communicated by H. Babaie

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Khoramak, S., Mahmoudi, F.T. Multi-agent hyperspectral and lidar features fusion for urban vegetation mapping. Earth Sci Inform 16, 165–173 (2023). https://doi.org/10.1007/s12145-022-00928-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12145-022-00928-y

Keywords

Search

Navigation