CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Hyperspectral Remote Sensing For Agricultural Management: A Survey

by B. D. Jadhav, P. M. Patil
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 106 - Number 7
Year of Publication: 2014
Authors: B. D. Jadhav, P. M. Patil
10.5120/18536-9750

B. D. Jadhav, P. M. Patil . Hyperspectral Remote Sensing For Agricultural Management: A Survey. International Journal of Computer Applications. 106, 7 ( November 2014), 38-43. DOI=10.5120/18536-9750

@article{ 10.5120/18536-9750,
author = { B. D. Jadhav, P. M. Patil },
title = { Hyperspectral Remote Sensing For Agricultural Management: A Survey },
journal = { International Journal of Computer Applications },
issue_date = { November 2014 },
volume = { 106 },
number = { 7 },
month = { November },
year = { 2014 },
issn = { 0975-8887 },
pages = { 38-43 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume106/number7/18536-9750/ },
doi = { 10.5120/18536-9750 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:38:48.848545+05:30
%A B. D. Jadhav
%A P. M. Patil
%T Hyperspectral Remote Sensing For Agricultural Management: A Survey
%J International Journal of Computer Applications
%@ 0975-8887
%V 106
%N 7
%P 38-43
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Hyperspectral sensors are devices that acquire images with narrow bands (less than 20nm) with continuous measurement. It extracts spectral signatures of objects or materials to be observed. Hyperspectral have more than 200 bands. Hyperspectral remote sensing has been used over a wide range of applications, such as agriculture, forestry, geology, ecological monitoring, atmospheric compositions and disaster monitoring. This review details concept of hyperspectral remote sensing; processing of hyperspectral data. It also focuses on the application of hyperspectral imagery in agricultural development. For example, hyperspectral image processing is used in the monitoring of plant diseases, insect pests and invasive plant species; the estimation of crop yield; and the fine classification of crop distributions.

References
  1. Goetz J. A. H. F. ,G. Vane, J. E. Solomon, B. N. Rock,"Imaging Spectrometry for Earth Remote Sensing", Science, 1985, vol. 228, pp. 1147-1153.
  2. Charez Jr, P. S. Kwartenz " Extracting spectral contrast in Landsat Thematic Mapper image data using selective component analysis", International Journal of Photogrammetric Engineering and Remote sensing, 1989, Vol. 55,3 pp 339-348.
  3. Xian –Zhong shi, Mehrooz Aspandia David, "Using Hyperspectral data PLSR modeling to acess acid Sulphate Soil in subsurface", Journal of Soils and Sediments , Springer, 2014.
  4. Thenkabail P. S. , J. G. Lyon, A. Huete, "Hyperspectral Remote Sensing of Vegetation", CRC Press, 2012.
  5. Haboudane, D. ; Miller, J. R. ; Pattey, E. ; Zarco-Tejada, P. J. & Strachan, I. B. , "Hyperspectral vegetation indices and novel algorithms for redicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture," Remote Sensing of Environment, 2004, vol. 90, pp337-352.
  6. Clark, Roger N. ,"Spectroscopy of Rocks and Minerals, and Principles of Spectroscopy". In Renz, Andrew N. (ed), Remote Sensing for the Earth Sciences: Manual of Remote Sensing (3rd ed. ), 1999 . Vol 3. New York: John Wiley & Sons, pp. 3-58.
  7. Zhang N. , M. Wang, N. Wang, "Precision agriculture-a worldwide overview", Computers and Electronics in Agriculture, 2002,vol. 36, pp. 113-132.
  8. Kaufmann H. , K. Segl, L. Guanter, S. Hofer, K. -P. Foerster, T. Stuffler, A. Mueller, R. Richter, H. Bach, P. Hostert, C. Chlebek,"Environmental Mapping and Analysis Program (EnMAP) - Recent Advances and Status", IEEE Geoscience and Remote Sensing Symposium, IGARSS 2008, vol. 4, pp. 109-112
  9. Ben-Dor, E. , Irons, J. R. , and Epema, G. F. Soil Reflectance. In Renz, Andrew N. (ed), "Remote Sensing for the Earth Sciences" Manual of Remote Sensing (3rd ed. ), Vol 3. New York: John Wiley & Sons, . 1999, pp. 111-188.
  10. Richter R, "Hyperspectral Sensors for Military Applications. In Emerging EO Phenomenology", Meeting Proceedings RTO-MP-SET-094,2005.
  11. Varshney P. K. , M. K. Arora, , "Advanced Image Processing Techniques for Remotely Sensed Hyperspectral Data", Springer, 2004.
  12. Green, A. A. , Berman, M. , Switzer, B. , and Craig, M. D. , "A transformation for ordering multispectral data in terms of image quality with implications for noise removal", IEEE Transactions on Geoscience and Remote Sensing, 1988, v. 26, no. 1, p. 65 - 74.
  13. Boardman, J. W. , Kruse, F. A. , and Green, R. O. , "Mapping target signatures via partial unmixing of AVIRIS data: in Summaries", Fifth JPL Airborne Earth Science Workshop, JPL Publication 95-1, v. 1, p. 23-26. Boardman, J. W. , 1993, "Automated spectral unmixing of AVIRIS data using convex geometry concepts" in Summaries, Fourth JPL Airborne Geoscience Workshop, JPL Publication 93-26, 1995, v. 1, p. 11 - 14.
  14. Boardman, J. W. , "Leveraging the high dimensionality of AVIRIS data for improved sub-pixel target unmixing and rejection of false positives: mixture tuned matched filtering", in Summaries of the Seventh Annual JPL Airborne Geoscience Workshop, Pasadena, CA, 1998, p. 55
  15. Kruse, F. A. , and Lefkoff, A. B. , Knowledge-based geologic mapping with imaging spectrometers: Remote Sensing Reviews, Special Issue on NASA Innovative Research Program (IRP) results, 1993, v. 8, p. 3 - 28.
  16. Kruse, F. A. , Lefkoff, A. B. , and Dietz, J. B. , Expert System-Based Mineral Mapping in northern DeathValley, California/Nevada using the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS): Remote Sensing of Environment, Special issue on AVIRIS, May-June 1993, v. 44, p. 309 - 336.
  17. Kruse, F. A. , Lefkoff, A. B. , Boardman, J. B. , Heidebrecht, K. B. , Shapiro, A. T. , Barloon, P. J. , and Goetz, A. F. H. , The Spectral Image Processing System (SIPS) - Interactive Visualization and Analysis of Imaging Spectrometer Data: Remote Sensing of Environment, Special issue on AVIRIS, May-June 1993, v. 44, p. 145- 163.
  18. Chen, J. Y. , and Reed, I. S. , "A detection algorithm for optical targets in clutter", IEEE Trans. on Aerosp. Electron. Syst. , 1987,v. AES-23, no. 1.
  19. Stocker, A. D. , Reed, I. S. , and Yu, X. , "Multidimensional signal processing for electro optical target detection", Proc, SPIE Int. Soc. Opt. Eng. , 1990, v. 1305.
  20. Harsanyi, J. C. , and Chang, C. I. , "Hyperspectral image classification and dimensionality reduction", An orthogonal subspace projection approach: IEEE Trans. Geosci. and Remote Sens. , 1994, v. 32, p. 779-785.
  21. Evangelista P. H. , T. J. Stohlgren, J. T. Morisette, S. Kumar, "Mapping Invasive Tamarisk (Tamarix): A Comparison of Single-Scene and Time-Series Analyses of Remotely Sensed Data", Remote Sensing, 2009, vol. 1, issue 3, pp. 519-533.
  22. Wang L. , "Invasive species spread mapping using multi-resolution remote sensing data", The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. XXXVII. Part B2. Beijing 2008
  23. Bajcsy P. , P. Groves, "Methodology For Hyperspectral Band Selection", Photogrammetric Engineering and Remote Sensing, 2004, Vol. 70, no. 7, pp. 793-802.
  24. Govender M, , K. Chetty, H. Bulcock, , "A review of hyperspectral remote sensing and its application in vegetation and water resource studies", Water SA, 2007,vol. 33.
  25. Gat N. , H. Erives, G. J. Fitzgerald, S. R. Kaffka, S. J. Maas, "Estimating sugar beet yield using AVIRIS-derived indices" In Summaries of the 9th JPL Airborne Earth Science Workshop. unpaginated CD. Jet Propulsion Laboratory, Pasadena, 2000.
  26. Yang C. , "Airborne Hyperspectral Imagery for Mapping Crop Yield Variability", Geography Compass. Vol. 3/5, pp. 1717-1731, 2009.
  27. Dalponte M. , M. , L. Bruzzone, D. Gianelle, 2009, "The role of spectral resolution and classifier complexity in the analysis of hyperspectral images of forest areas", Remote Sensing of Environment, vol. 113, pp. 2345-2355.
  28. Yen-Ben Cheng et al. , "Estimating vegetation water content with hyperspectral data for different canopy scenarios Relationships between AVIRIS and MODIS indexes", Remote sensing environment, 2006, vol. 105, pp. 354-366.
  29. Naveen J. P. Anne et al. , "Modeling soil parameters using hyperspectral image reflectance in subtropical coastal wetlands" International Journal of Applied Earth Observation and Geoinformation, 2014, pp. 47-56.
  30. Bannari A. , A. Pacheco, K. Staenz, H. McNairn, K. Omari, "Estimating and mapping crop residues cover on agricultural lands using hyperspectral and IKONOS data," Remote Sensing of Environment, 2006, vol. 104, 447-459.
  31. Zhang N. , M. Wang, N. Wang, "Precision agriculture-a worldwide overview", Computers and Electronics in Agriculture, 2002, vol. 36, pp. 113-132
  32. http://speclab. cr. usgs. gov/spectral-lib. htmlAccess date: 25. 03. 2013.
  33. Atmospheric CoRection (ACRON) http://www. aigllc. com/pdf/acorn4_ume. pdf,Accces date:25/3/13.
  34. ATmosphericCORrection,http://www. dlr. de/eoc/Portaldata/60/Resources/dokumente/5_tech_mod/atcor4_manual_2012. pdf,Accces date: 25. 03. 2013.
Index Terms

Computer Science
Information Sciences

Keywords

Hyperspectral Multispectral Remote sensing Spectrometer

Powered by PhDFocusTM