Neuro-textural classification of Indian urban environment
Pathak, Virendra & Dikshit, Onkar (2005) Neuro-textural classification of Indian urban environment. Geocarto International, 20(3), pp. 65-73.
Experiments were conducted to see the effects of a set of factors on the Resilient backpropagation (Rprop) artificial neural network classification of an Indian urban environment using IRS-1C satellite data. Factors investigated were sample size, number of neurons in hidden layers and number of epochs. The effect of including texture information in the form of neighbourhood information and grey level co-occurance matrix (GLCM) features in the classification process has been explored. Statistically similar overall classification accuracy is achieved for Rprop and Gaussian maximum likelihood classification (GMLC). Investigations have revealed that a large sample size gave higher test accuracy; variation in number of neurons in hidden layer did not affect the overall classification accuracy significantly; lesser number of epochs resulted in higher overall test accuracy. Incorporation of texture information by both approaches improved classification accuracy in a statistically significant manner.
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|Item Type:||Journal Article|
|Keywords:||ANN, GMLC, GLCM, Texture, Rprop|
|Subjects:||Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > GEOMATIC ENGINEERING (090900) > Photogrammetry and Remote Sensing (090905)|
|Divisions:||Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
Past > Schools > School of Urban Development
|Copyright Owner:||Copyright 2005 Taylor & Francis|
|Copyright Statement:||This is an electronic version of an article published in Pathak, Virendra and Dikshit, Onkar (2005) Neuro-textural classification of Indian urban environment. Geocarto International, 20(3). pp. 65-73. Geocarto International is available online at informaworld(TM) http://www.geocarto.com/geocarto.html|
|Deposited On:||17 Feb 2009 03:46|
|Last Modified:||29 Feb 2012 13:57|
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