The influence of training errors, context and number of bands in the accuracy of image classification

Abstract

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.