A detailed comparison of neuro-fuzzy estimation of sub-pixel land-cover composition from remotely sensed data

Mixed pixels, which do not follow a known statistical distribution that could be parameterized, are a major source of inconvenience in classification of remote sensing images. This paper reports on an experimental study designed for the in-depth investigation of how and why two neuro-fuzzy classification schemes, whose properties are complementary, estimate sub-pixel land cover composition from remotely sensed data. The first classifier is based on the Fuzzy Multilayer Perceptron proposed by Pal and Mitra; the second classifier consists of a Two-Stage Hybrid (TSH) learning scheme whose unsupervised first stage is based on the Fully self-Organizing Simplified Adaptive Resonance Theory clustering network proposed by Baraldi. Results of the two neuro-fuzzy classifiers are assessed by means of specific evaluation tools designed to extend conventional descriptive and analytical statistical estimators to the case of multi-membership in classes. When a synthetic data set consisting of pure and mixed pixels is processed by the two neuro-fuzzy classifiers, experimental results show that: i) the two neuro-fuzzy classifiers perform better than the traditional MLP; ii) classification accuracies of the two neurofuzzy classifiers are comparable; and iii) the TSH classifier requires to train less background knowledge (ground data) than FMLP.