Maximum Likelihood Classification of High-Resolution Multispectral Data Over Arasikere Semi-urban Area

Authors

  • A L Choodarathnakara  Department. of Electronics & Communication Engineering, GEC, Kushalnagar, Kodagu, Karnataka, India
  • Sujith J  Quadgen Wireless Solutions, Bangalore, Karnataka, India
  • Priyanka R  Department. of Electronics & Communication Engineering, GEC, Kushalnagar, Kodagu, Karnataka, India
  • Prathibha Rani P S  Department. of Electronics & Communication Engineering, GEC, Kushalnagar, Kodagu, Karnataka, India
  • Anupama S J  Department. of Electronics & Communication Engineering, GEC, Kushalnagar, Kodagu, Karnataka, India
  • Arpitha A V  Department. of Electronics & Communication Engineering, GEC, Kushalnagar, Kodagu, Karnataka, India

Keywords:

Remote Sensing, Semi-urban Area, Maximum Likelihood Classification, Erdas Imagine

Abstract

Remote sensing refers to the science of identification of earth surface features and estimation of their geophysical properties using electromagnetic radiation as a medium of interaction. Spectral, Spatial, Temporal and Polarization signatures are major characteristics of the sensor or target, which facilitates target discrimination. Earth surface data are seen by the sensors in different wavelengths (Reflected, Scattered and/or Emitted) is radiometrically and geometrically corrected before extraction of spectral information. Image classification is the process of categorizing all the pixels automatically in an image into a finite number of land use/land cover classes. The major operational application themes, in which India has extensively used remote sensing data are agriculture, forestry, water resources, LU/LC, urban sprawl, geology environment, coastal zone, marine resources, snow and glacier, disaster and mitigation, infrastructure development etc. In Remote Sensing, image classification approaches can be grouped as supervised and unsupervised, or parametric and non-parametric, or hard and soft (fuzzy) classification, or per pixel, sub pixel and per field. Based on whether training is used or not the classifiers are classified into supervised and unsupervised classifiers. In this paper maximum likelihood supervised classification technique is employed on remotely sensed satellite image data for classification of urban features. The accuracy assessment was conducted based on Overall Classification Accuracy (OCA) and Kappa Statistics. This experiment was conducted using Erdas Imagine V9.2 RS software. Finally, the suitability of MLC is verified on Arasikere Semi-urban area and Overall Classification Accuracy 83.33% and 90.15% was obtained for 180 training sites with 132 validation sites and 562 training sites with 132 training sites respectively.

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International Journal of Scientific Research in Science, Engineering and Technology

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Published

2017-06-30

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Volume 3, Issue 3, May-June-2017

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Research Articles

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How to Cite

[1]
A L Choodarathnakara, Sujith J, Priyanka R, Prathibha Rani P S, Anupama S J, Arpitha A V, " Maximum Likelihood Classification of High-Resolution Multispectral Data Over Arasikere Semi-urban Area, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 3, Issue 3, pp.287-296, May-June-2017.