Evolving Neural Network Using Variable String Genetic Algorithm for Color Infrared Aerial Image Classification

Coastal wetlands are characterized by complex patterns both in their geomorphic and ecological features. Besides field observations, it is necessary to analyze the land cover of wetlands through the color infrared (CIR) aerial photography or remote sensing image. In this paper, we designed an evolving neural network classifier using variable string genetic algorithm (VGA) for the land cover classification of CIR aerial image. With the VGA, the classifier that we designed is able to evolve automatically the appropriate number of hidden nodes for modeling the neural network topology optimally and to find a near-optimal set of connection weights globally. Then, with backpropagation algo-rithm (BP), it can find the best connection weights. The VGA-BP classifier, which is derived from hybrid algorithms mentioned above, is demonstrated on CIR images classification effectively. Compared with standard classifiers, such as Bayes maximum-likelihood classifier, VGA classifier and BP-MLP (multi-layer perception) classifier, it has shown that the VGA-BP classifier can have better performance on highly resolution land cover classification.