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Advanced Machine Learning Algorithms based Free and Open Source Packages for Landsat ETM+ Data Classification
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Uttam Kumar1,2,3          Anindita Dasgupta1          Chiranjit Mukhopadhyay2           T.V. Ramachandra1,3,4,*
1Energy and Wetlands Research Group, Centre for Ecological Sciences [CES], 2Department of Management Studies, 3Centre for Sustainable Technologies (astra),
4Centre for infrastructure, Sustainable Transportation and Urban Planning [CiSTUP], Indian Institute of Science, Bangalore – 560012, India.
*Corresponding author: cestvr@ces.iisc.ernet.in

ABSTRACT

Detailed and accurate inventorying and mapping of land use (LU) at a regional/national scale is now possible with the availability of various medium spatial resolution sensors (such as Landsat, IRS LISS-III/IV, etc.). The LU information are derived using machine learning algorithms which are mainly dependent on the spectral properties of objects in the bands to assign them into a user defined class label. Designing a suitable image processing procedure is a prerequisite for successful classification of remotely sensed data into thematic information. Use of multiple features and selection of a suitable classification method are especially significant for improving classification accuracy. In this context, this paper reviews six advanced machine learning techniques such as Decision Tree, K-Nearest Neighbour, Neural Network (NN), Random Forest, Contextual Classification using sequential maximum a posteriori estimation (SMAP), and Support Vector Machine for Landsat ETM+ data classification using Free and Open Source (FOS) Packages along with the algorithmic descriptions. The ETM+ data classification results showed that SMAP classifier gave best performance with 89% overall accuracy and 0.8596 kappa followed by KNN with 87% overall accuracy and 0.8314 kappa while NN performed the last with 75% accuracy and 0.7142 kappa.

Keywords - FOSS; machine learning; Landsat ETM+; classification

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Citation : Uttam Kumar, Anindita Dasgupta, Chiranjit Mukhopadhyay and Ramachandra. T.V., 2012, Advanced Machine Learning Algorithms based Free and Open Source Packages for Landsat ETM+ Data Classification., Proceedings of the OSGEO-India: FOSS4G 2012- First National Conference "OPEN SOURCE GEOSPATIAL RESOURCES TO SPEARHEAD DEVELOPMENT AND GROWTH” 25-27th October 2012, @ IIIT Hyderabad , pp. 1-7.
* Corresponding Author :
Dr. T.V. Ramachandra
Energy & Wetlands Research Group, Centre for Ecological Sciences, Indian Institute of Science, Bangalore – 560 012, India.
Tel : +91-80-2293 3099/2293 3503-extn 107,      Fax : 91-80-23601428 / 23600085 / 23600683 [CES-TVR]
E-mail : cestvr@ces.iisc.ernet.in, energy@ces.iisc.ernet.in,     Web : http://wgbis.ces.iisc.ernet.in/energy, http://ces.iisc.ernet.in/grass
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