Using Learning Cellular Automata for Post Classification Satellite Imagery

When classifying an image, there might be several pixels having near among probability, spectral angle or mahalanobis distance which are normally regarded as unclassified or misclassified. These pixels so called chaos pixels exist because of radiometric overlap between classes, accuracy of parameters estimated, etc. which lead to some uncertainty in assigning a label to the pixels. To resolve such uncertainty, some post classification algorithms like Majority, Transition matrix and Probabilistic Label Relaxation (PLR) are traditionally used. Unfortunately, these techniques are inflexible so a desired accuracy can not be achieved. Therefore, techniques are needed capable of improving themselves automatically. Learning Automata have been used to model biological learning systems in computer science to find an optimal action offered by an environment. In this research, we have used pixels as the cellular automata and the thematic map as the environment to design a selfimproving post classification technique. Each pixel interacts with the thematic map in a series of repetitive feedback cycles. In each cycle, the pixel chooses a class (an action), which triggers a response from the thematic map (the environment); the response can either be a reward or a penalty. The current and past actions performed by the pixel and its neighbours define what the next action should be. In fact, by learning, the automata (pixels) change the class probability and choose the optimal class adapting itself to the environment. For learning, tow criteria for local and global optimization, the entropy of each pixel and Producer's Accuracy of classes have been used. Tests were carried out using a subset of AVIRIS imagery. The results showed an improvement in the accuracy of test samples. In addition, the approach was compared with PLR, the results of which suggested high stability of the algorithm and justified its advantages over the current post classification techniques.