SAR Image Classification Based on Brushlet and Self-Adaptive Ridgelet Neural Network

Aiming at the defect that BP neural network classification model takes a long time for network training and the condition that wavelet network model lacks of direction information description, the paper presents a method for SAR image classification based on Brushlet and self-adaptive ridgelet neural network. The method extracts the energy and phase feature of SAR image texture through the Brushlet transformation, and inputs the feature vector that describes the energy and phase information into self-adaptive ridgelet neural network for training and classification. The contrast experiment indicates that the classification method proposed in this paper is rapid and accurate, and outperforms the traditional methods.