Researchpaper Comparative Analysis Of Different Supervised Classification Techniques For Spatial Land
International Journal of Scientific & Engineering Research, Volume 4, Issue 7, July-2013
ISSN 2229-5518
1938
“Comparative Analysis of Different Supervised
Classification Techniques for Spatial Land
Use/Land Cover Pattern Mapping Using RS and GIS”
Amol D.Vibhute1, Ajay D.Nagne2, Bharti W. Gawali 3, Suresh C.Mehrotra4
Abstract—The present study highlights the advantages of Remote Sensing (RS) and Geographic Information System (GIS) techniques for analyzing the land use/land cover mapping for Aurangabad region of Maharashtra, India. Single date, cloud free IRS-Resourcesat-1 LISSIII data was used for further classification on training set for supervised classification. Some image enhancement techniques were also performed to improve the satellite imagery for better visual interpretation. ENVI 4.4 image analysis tool and Arc GIS10 software were used for data processing and analysis. Maximum Likelihood, mahalanobis distance, minimum distance and parallelepiped classifiers were performed for LULC classification in this study. The main objective of this study is to develop land use/land cover map for Aurangabad region to predict the possible applications of LULC. Level-II of Anderson classification was also performed for LULC mapping analysis. It is observed that maximum area is covered by barren land, agricultural area (with crops and without crops), hilly area, built-up area and water bodies’ etc respectively. The result of the classifications suggests that, each used and covered type were best classified by the Maximum likelihood classification technique.
Index Terms—Image Enhancement, Land use/Land cover, Maximum Likelihood Classification, Remote Sensing and GIS, Supervised
Classification.
IJSER
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1 INTRODUCTION
F
rom the past two decades, the economic development and population growth have triggered a rapid change to the Earth 's land cover and the indication shows that the pace of these changes is going to increase in the coming future.
ISSN 2229-5518
1938
“Comparative Analysis of Different Supervised
Classification Techniques for Spatial Land
Use/Land Cover Pattern Mapping Using RS and GIS”
Amol D.Vibhute1, Ajay D.Nagne2, Bharti W. Gawali 3, Suresh C.Mehrotra4
Abstract—The present study highlights the advantages of Remote Sensing (RS) and Geographic Information System (GIS) techniques for analyzing the land use/land cover mapping for Aurangabad region of Maharashtra, India. Single date, cloud free IRS-Resourcesat-1 LISSIII data was used for further classification on training set for supervised classification. Some image enhancement techniques were also performed to improve the satellite imagery for better visual interpretation. ENVI 4.4 image analysis tool and Arc GIS10 software were used for data processing and analysis. Maximum Likelihood, mahalanobis distance, minimum distance and parallelepiped classifiers were performed for LULC classification in this study. The main objective of this study is to develop land use/land cover map for Aurangabad region to predict the possible applications of LULC. Level-II of Anderson classification was also performed for LULC mapping analysis. It is observed that maximum area is covered by barren land, agricultural area (with crops and without crops), hilly area, built-up area and water bodies’ etc respectively. The result of the classifications suggests that, each used and covered type were best classified by the Maximum likelihood classification technique.
Index Terms—Image Enhancement, Land use/Land cover, Maximum Likelihood Classification, Remote Sensing and GIS, Supervised
Classification.
IJSER
—————————— ——————————
1 INTRODUCTION
F
rom the past two decades, the economic development and population growth have triggered a rapid change to the Earth 's land cover and the indication shows that the pace of these changes is going to increase in the coming future.
References: 2(1), 2008. Journal of Theoretical and Applied Information Technology, pp. 80-86, JATIT & LLS, 2005 – 2011. Journal of Geomatics And Geosciences, vol. 2(3), pp. 911-923, 2012 Vemu Sreenivasulu and Pinnamaneni Udaya Bhaskar, “Change Detection in Landuse and landcover using Remote Sensing and GIS Techniques”, International Journal of Engineering Science and Technology, vol. 2(12), pp. 7758-7762, 2010. Fuzzy C-Mean Algorithm”, International Journal of Geomatics and Geosciences, vol. 2(2), pp. 613-626, 2011.