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Relative efficiency of larsys classifiers in soil mapping

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Abstract

Launching of Landsat series and flow of data within and beyond the visual spectrum furnished a potent tool for data acquisition to the earth resources scientists for expanding the teritories of knowledge. Increased capability of computer technology made many advancements possible in the field of Remote Sensing.

LARS, Purdue, USA has developed several methodologies for abstracting information from Landsat products in various fields of application. The methods employing algorithms of maximum likelihood and minimum distance have been compared applying the techniques of pooling and deleting of LARS to classify soils of Hapur area, Uttar Pradesh, India. It was found that the maximum likelihood yielded a map with better dispostion of soil-scape but the minimum distance method, by deleting, is seen to be very efficient in class combination and CPU time. The results are discussed in this paper with illustrations.

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References

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Author information

Correspondence to K. V. Seshagiri Rao.

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Seshagiri Rao, K.V., Karale, R.L. Relative efficiency of larsys classifiers in soil mapping. J Indian Soc Remote Sens 16, 31–38 (1988). https://doi.org/10.1007/BF02998736

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Keywords

  • Remote Sensing
  • Landsat
  • Field Class
  • Landsat Data
  • Gangetic Plain