Identification, Classification and Mapping of Surface Soil Types Using Hyperspectral Remote Sensing Datasets

Authors

  • Amol D. Vibhute  Geospatial Technology Research laboratory, Department of Computer Science & IT, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, Maharashtra, India
  • Karbhari V. Kale  Department of Computer Science & IT, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, Maharashtra, India
  • Rajesh K. Dhumal  Geospatial Technology Research laboratory, Department of Computer Science & IT, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, Maharashtra, India
  • Ajay D. Nagne  Geospatial Technology Research laboratory, Department of Computer Science & IT, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, Maharashtra, India
  • Suresh C. Mehrotra  Department of Computer Science & IT, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, Maharashtra, India

Keywords:

Soil classification, Hyperspectral data, Minimum Noise Fraction, Spectral Angle Mapper, Dimensionality reduction, Accuracy Assessment

Abstract

The traditional methods of soil analysis are tedious and they do not fulfill the rapid requirements of spatiotemporal variability. The present study highlights the use of hyperspectral remote sensing (HRS) datasets for soil classification. The Minimum Noise Fraction (MNF) method was implemented for dimensionality reduction of huge Hyperion data. First, ten MNFs were provided precious information. The Analytical Spectral Device (ASD) non-imaging spectroradiometer was used for recording the collected 74 soil samples. The reference spectra was analyzed and used for Hyperion image classification. The Spectral Angle Mapper (SAM) method was computed after dimensionality reduction by MNF method for soil classification. The overall accuracy of SAM classifier was 91.77 percent with Kappa Value 0.89. The black cotton soil, lateritic soil and sand dunes of surface soil types were identified, classified and mapped. The outcome of the present study is essential for digital soil analysis and its mapping of heterogeneous area.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2018-02-28

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Volume 3, Issue 1, January-February-2018

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Research Articles

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How to Cite

[1]
Amol D. Vibhute, Karbhari V. Kale, Rajesh K. Dhumal, Ajay D. Nagne, Suresh C. Mehrotra, " Identification, Classification and Mapping of Surface Soil Types Using Hyperspectral Remote Sensing Datasets , IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 1, pp.921-932, January-February-2018.