Contextual classification in image analysis: an assessment of accuracy of ICM
Computational Statistics & Data Analysis
Remote Sensing Digital Image Analysis: An Introduction
Remote Sensing Digital Image Analysis: An Introduction
Image processing by simulated annealing
IBM Journal of Research and Development - High-density magnetic recording
Image Analysis, Random Fields and Markov Chain Monte Carlo Methods: A Mathematical Introduction (Stochastic Modelling and Applied Probability)
Estimation of Markov random field prior parameters using Markov chain Monte Carlo maximum likelihood
IEEE Transactions on Image Processing
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We present the assessment of two classification procedures using both a Monte Carlo experiment and real data. Classification performance is hard to assess with generality due to the huge number of variables involved. We consider the problem of classifying multispectral optical imagery with pointwise Gaussian Maximum Likelihood (ML) and contextual ICM (Iterated Conditional Modes), with and without errors in the training stage. Two experimental setups were considered in order to assess the influence of using partial and low-quality information and to make a quantitative comparison of ML and ICM in real situations. Using simulation the ground truth is known and, therefore, precise comparisons are possible. The contextual approach proved to be superior to the pointwise one, at the expense of requiring more computational resources. Quantitative and qualitative results are discussed.