Opening the black box of neural networks for remote sensing image classification

Neural networks, which make no assumption about data distribution, have achieved improved image classification results compared to traditional methods. Unfortunately, a neural network is generally perceived as being a 'black box'. It is extremely difficult to document how specific classification decisions are reached. Fuzzy systems, on the other hand, have the capability to represent classification decisions explicitly in the form of fuzzy 'if-then' rules. However, the construction of a knowledge base, especially the fine-tuning of the fuzzy set parameters of the fuzzy rules in a fuzzy expert system, is a tedious and subjective process. This research has developed a new, improved neuro-fuzzy image classification system based on the synergism between neural networks and fuzzy expert systems. It incorporates the best of both technologies and compensates for the shortcomings of each. The learning algorithms of neural networks developed here are used to automate the derivation of fuzzy set parameters for the fuzzy 'if-then' rules in a fuzzy expert system. The rules obtained, in symbolic form, facilitate the understanding of the neural network based image classification system. In addition, the image classification accuracy obtained from the improved neuro-fuzzy system was significantly superior to those of the back-propagation based neural network and the maximum likelihood approaches.