Neuro-textural classification of Indian urban environment

Abstract

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.