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Multispectral Image Analysis Using Decision Trees, Arun D. Kulkarni, Anmol Shrestha
Multispectral Image Analysis Using Decision Trees, Arun D. Kulkarni, Anmol Shrestha
Arun Kulkarni
Many machine learning algorithms have been used to classify pixels in Landsat imagery. The maximum likelihood classifier is the widely-accepted classifier. Non-parametric methods of classification include neural networks and decision trees. In this research work, we implemented decision trees using the C4.5 algorithm to classify pixels of a scene from Juneau, Alaska area obtained with Landsat 8, Operation Land Imager (OLI). One of the concerns with decision trees is that they are often over fitted with training set data, which yields less accuracy in classifying unknown data. To study the effect of overfitting, we have considered noisy training set data …
Generating Classification Rules From Training Samples, Arun D. Kulkarni
Generating Classification Rules From Training Samples, Arun D. Kulkarni
Arun Kulkarni
In this paper, we describe an algorithm to extract classification rules from training samples using fuzzy membership functions. The algorithm includes steps for generating classification rules, eliminating duplicate and conflicting rules, and ranking extracted rules. We have developed software to implement the algorithm using MATLAB scripts. As an illustration, we have used the algorithm to classify pixels in two multispectral images representing areas in New Orleans and Alaska. For each scene, we randomly selected 10 per cent of the samples from our training set data for generating an optimized rule set and used the remaining 90 per cent of samples …