Comparison between Possibilistic c-Means (PCM) and Artificial Neural Network (ANN) Classification Algorithms in Land use/ Land cover Classification

Abstract

The ability of the human brain and eye to recognize features can be modelled using the artificial neural network (ANN). The neurons are trained to recognize individual pixels and cluster them correctly in their respective classes. This is a very complex process especially when a multi-layer hierarchical system is used. A well trained ANN however, is capable of performing classification better than a human being since it is devoid of subjectivity and human error. In this research, the output of an artificial neural network algorithm was compared with the Possibilistic c-Means an improvement of the fuzzy c-Means- on both moderate resolution Landsat8 and a high resolution Formosat2 images. The Formosat2 image comes with an 8m spectral resolution on the multispectral data. This multispectral image data was resampled to 10m in order to maintain a uniform ratio of 1:3 against Landsat8 image. Six classes were chosen for analysis including: Dense forest, eucalyptus, water, grassland, wheat and riverine sand. Using a standard false color composite (FCC), the six features reflected differently in the infrared region with wheat producing the brightest pixel values. Signature collection per class was therefore easily obtained for all classifications. The output of both ANN and FCM, were analyzed separately for accuracy and an error matrix generated to assess the quality and accuracy of the classification algorithms.

URL: http://ijkcdt.net/xml/09912/09912.pdf