Comparison of Neural Network and Maximum Likelihood Approaches in Image Classification

Classification of satellite images with the intent of land cover mapping is one of the remote sensing applications. The objective of this study is to compare the Neural Network and Maximum Likelihood approaches in land cover mapping by using high spatial resolution satellite images in Makkah city which is located in the semi-arid conditions in western of Saudi Arabia. Many algorithms are available for classification of satellite images. Maximum Likelihood classification method is widely used in many remote sensing applications and can be regard as one of the most reliable techniques. Pixels are assigned to the class of highest probability. Neural Network classification is based on training during a training phrase and the proper classification. Its can reduce the speckle or mixed pixel problem in the image. In this paper we were studying the performances of these methods for the purpose of land cover mapping. The experimental results of this work indicated that the Neural Networks algorithm with 89.3% overall accuracy and 0.820 Kappa Coefficient is more reliable than the Maximum Likelihood algorithm with 80.3% and 0.672 overall accuracy and Kappa coefficient, respectively.