Skip to main content
SpringerLink
Log in

Identification and Representation of Spectral Anomalies in an Abandoned Quarry by Remote Sensing

  • Conference paper
  • First Online:
Inventive Computation and Information Technologies

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 563))

  • 404 Accesses

Abstract

Soil pollution represents a problem that must be addressed by exploiting all the resources we have available. The transformation of the built environment requires a large amount of raw materials that are extracted from the quarries, exploiting to the full abandoned places often used as a deposit of materials harmful to humans. In this work, images detected in a specific hyperspectral aerial remote sensing campaign with Itres CASI 1500 sensor were analyzed. The measurements were stored in a georeferenced image with 36 levels, one for each detected wavelength. The hyperspectral images were post-processed using vegetation indices, PCA and RXD algorithms. The survey methodology made it possible to detect spectral anomalies that require greater investigation with specific methods.

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 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
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. Rodríguez-Eugenio N, McLaughlin M, Pennock D (2018) Soil pollution: a hidden reality. FAO

    Google Scholar 

  2. Mishra RK, Mohammad N, Roychoudhury N (2016) Soil pollution: causes, effects and control. Van Sangyan 3(1):1–14

    Google Scholar 

  3. Fayiga AO, Saha UK (2016) Soil pollution at outdoor shooting ranges: health effects, bioavailability and best management practices. Environ Pollut 216:135–145

    Article  Google Scholar 

  4. Ciaburro G (2021) Recycled materials for sound absorbing applications. Mater Sci Forum 1034:169–175

    Google Scholar 

  5. Milgrom T (2008) Environmental aspects of rehabilitating abandoned quarries: Israel as a case study. Landsc Urban Plan 87(3):172–179

    Article  Google Scholar 

  6. Τsolaki-Fiaka S, Bathrellos GD, Skilodimou HD (2018) Multi-criteria decision analysis for an abandoned quarry in the Evros Region (NE Greece). Land 7(2):43

    Article  Google Scholar 

  7. Ren X, Cai T, Wang X (2010) Effects of vegetation restoration models on soil nutrients in an abandoned quarry. J Beijing For Univ 32(4):151–154

    Google Scholar 

  8. Kivell PT (2021) Land reclamation through waste disposal. In: Waste location: spatial aspects of waste management, hazards and disposal, p 12

    Google Scholar 

  9. Gambardella C, Parente R, Ciambrone A, Casbarra M (2021) A principal components analysis-based method for the detection of cannabis plants using representation data by remote sensing. Data 6(10):108

    Article  Google Scholar 

  10. Campbell JB, Wynne RH (2011) Introduction to remote sensing. Guilford Press

    Google Scholar 

  11. Weiss M, Jacob F, Duveiller G (2020) Remote sensing for agricultural applications: a meta-review. Remote Sens Environ 236:111402

    Article  Google Scholar 

  12. Itres Research Limited homepage. https://itres.com/. Accessed 2022/05/10

  13. Acheroy M (2007) Mine action: status of sensor technology for close-in and remote detection of anti-personnel mines. Near Surf Geophys 5(1):43–55. https://doi.org/10.3997/1873-0604.2006017

    Article  Google Scholar 

  14. Cavalli RM, Colosi F, Palombo A, Pignatti S, Poscolieri M (2007) Remote hyperspectral imagery as a support to archaeological prospection. J Cult Herit 8(3):272–283. https://doi.org/10.1016/j.culher.2007.03.003

    Article  Google Scholar 

  15. Maathuis BHP, van Genderen JL (2004) A review of satellite and airborne sensors for remote sensing based detection of minefields and landmines. Int J Remote Sens 25(23):5201–5245. https://doi.org/10.1080/01431160412331270803

    Article  Google Scholar 

  16. Robledo L, Carrasco M, Mery D (2009) A survey of land mine detection technology. Int J Remote Sens 30(9):2399–2410. https://doi.org/10.1080/01431160802549435

    Article  Google Scholar 

  17. Rowlands A, Sarris A (2007) Detection of exposed and subsurface archaeological remains using multi-sensor remote sensing. J Archaeol Sci 34(5):795–803. https://doi.org/10.1016/j.jas.2006.06.018

    Article  Google Scholar 

  18. Motohka T, Nasahara KN, Oguma H, Tsuchida S (2010) Applicability of green-red vegetation index for remote sensing of vegetation phenology. Remote Sens 2(10):2369–2387

    Article  Google Scholar 

  19. Qi J, Chehbouni A, Huete AR, Kerr YH, Sorooshian S (1994) A modified soil adjusted vegetation index. Remote Sens Environ 48(2):119–126

    Article  Google Scholar 

  20. Ready P, Wintz P (1973) Information extraction, SNR improvement, and data compression in multispectral imagery. IEEE Trans Commun 21(10):1123–1131

    Article  Google Scholar 

  21. Ciaburro G (2022) Time series data analysis using deep learning methods for smart cities monitoring. In: Big data intelligence for smart applications. Springer, Cham, pp 93–116

    Google Scholar 

  22. Reed IS, Yu X (1990) Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution. IEEE Trans Signal Process 38:1760–1770

    Article  Google Scholar 

  23. Ciaburro G (2021) Security systems for smart cities based on acoustic sensors and machine learning applications. In: Machine intelligence and data analytics for sustainable future smart cities. Springer, Cham, pp 369–393

    Google Scholar 

  24. Shen L, Stopher PR (2014) Review of GPS travel survey and GPS data-processing methods. Transp Rev 34(3):316–334

    Article  Google Scholar 

  25. Kobayashi S, Sanga-Ngoie K (2008) The integrated radiometric correction of optical remote sensing imageries. Int J Remote Sens 29(20):5957–5985

    Google Scholar 

  26. Gilabert MA, González-Piqueras J, Garcia-Haro FJ, Meliá J (2002) A generalized soil-adjusted vegetation index. Remote Sens Environ 82(2–3):303–310

    Google Scholar 

  27. Ciaburro G (2021) An ensemble classifier approach for thyroid disease diagnosis using the AdaBoostM algorithm. In: Machine learning, big data, and IoT for medical informatics. Academic Press, pp 365–387

    Google Scholar 

  28. Ciaburro G (2020) Sound event detection in underground parking garage using convolutional neural network. Big Data Cogn Comput 4(3):20

    Article  Google Scholar 

  29. Imani M (2017) RX anomaly detector with rectified background. IEEE Geosci Remote Sens Lett 14(8):1313–1317

    Article  Google Scholar 

  30. Mehmood A, Nasrabadi NM (2011) Kernel wavelet-Reed–Xiaoli: an anomaly detection for forward-looking infrared imagery. Appl Opt 50(17):2744–2751

    Article  Google Scholar 

  31. Ranganathan G (2020) Real time anomaly detection techniques using PySpark frame work. J Artif Intell 2(01):20–30

    Google Scholar 

  32. Shakya S, Pulchowk LN, Smys S (2020) Anomalies detection in fog computing architectures using deep learning. J Trends Comput Sci Smart Technol 1(2020):46–55

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Benecon University Consortium, Naples, Italy

    C. Gambardella & R. Parente

Authors
  1. C. Gambardella
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. R. Parente
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to R. Parente .

Editor information

Editors and Affiliations

  1. Department of Computer Science Engineering, RVS Technical Campus, Coimbatore, Tamil Nadu, India

    S. Smys

  2. Department of Computer Science, College of Science and Technology, Texas Southern University, Houston, TX, USA

    Khaled A. Kamel

  3. Department of Business Administration, The Gerald Schwartz School of Business, StFX University, Antigonish, NS, Canada

    Ram Palanisamy

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Gambardella, C., Parente, R. (2023). Identification and Representation of Spectral Anomalies in an Abandoned Quarry by Remote Sensing. In: Smys, S., Kamel, K.A., Palanisamy, R. (eds) Inventive Computation and Information Technologies. Lecture Notes in Networks and Systems, vol 563. Springer, Singapore. https://doi.org/10.1007/978-981-19-7402-1_34

Download citation

  • DOI: https://doi.org/10.1007/978-981-19-7402-1_34

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-7401-4

  • Online ISBN: 978-981-19-7402-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation