Random forest algorithm with derived geographical layers for improved classification of remote sensing data

Kumar, Uttam and Dasgupta, Anindita and Mukhopadhyay, Chiranjit and Ramachandra, TV (2011) Random forest algorithm with derived geographical layers for improved classification of remote sensing data. In: 2011 Annual IEEE India Conference (INDICON), 16-18 Dec. 2011, Hyderabad, India.

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Abstract

Effective conservation and management of natural resources requires up-to-date information of the land cover (LC) types and their dynamics. The LC dynamics are being captured using multi-resolution remote sensing (RS) data with appropriate classification strategies. RS data with important environmental layers (either remotely acquired or derived from ground measurements) would however be more effective in addressing LC dynamics and associated changes. These ancillary layers provide additional information for delineating LC classes' decision boundaries compared to the conventional classification techniques. This communication ascertains the possibility of improved classification accuracy of RS data with ancillary and derived geographical layers such as vegetation index, temperature, digital elevation model (DEM), aspect, slope and texture. This has been implemented in three terrains of varying topography. The study would help in the selection of appropriate ancillary data depending on the terrain for better classified information.

Item Type:	Conference Proceedings
Publisher:	IEEE
Additional Information:	Copyright of this article belongs to IEEE.
Keywords:	Bangalore; Himalaya; Western Ghats; Classification; Land Cover; Random Forest; Remote Sensing
Department/Centre:	Division of Biological Sciences > Centre for Ecological Sciences Division of Interdisciplinary Sciences > Center for Infrastructure, Sustainable Transportation and Urban Planning (CiSTUP) Division of Interdisciplinary Sciences > Management Studies
Date Deposited:	10 May 2013 09:36
Last Modified:	10 May 2013 09:36
URI:	http://eprints.iisc.ac.in/id/eprint/46355

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