Joint-classification change detection based on improved fuzzy ARTMAP

Post-Classification Comparison(PCC) method is widely used in change detection for remote sensing images, but it is affected by a significant cumulative error caused by single remote sensing image classification during change detection, which leads to the excessive evaluation of changed types and quantity. To solve this problem, this paper proposes a change detection method for remote sensing images based on Adaptive Resonance Theory Mapping (ARTMAP) neural network. Similarity matrix is constructed by spectral feature vectors. Then the threshold value of similarity is obtained, which is used to control the joint-classification classifier based on the ARTMAP neural network. In addition, an adaptive algorithm of vigilance parameter is introduced to the classification process of fuzzy ARTMAP neural network. The experimental results obtained on remote sensing images show that the proposed method not only accurately classifies the unchanged geographical information in different temporal images into the same class, but also reduces the cumulative error and improves the accuracy of change detection compared with other methods.