A novel neural approach for unsupervised change detection using SOM clustering for pseudo-training set selection followed by CSOM classifier

This paper proposes a novel neural model for unsupervised change detection in time series of multispectral remote sensing imagery using clustering with Self-Organizing Map (SOM) for automatic pseudo-training sample set selection cascaded with Concurrent Self-Organizing Maps (CSOM) classifier. The proposed algorithm has the following steps: (a) computation of difference image (DI) corresponding to the magnitudes of Spectral Change Vectors (SCVs); (b) SOM clustering to automatically deduce the SCV domain quantization parameters defining the pseudo-training sample set regions (changed, unchanged and uncertain); (c) CSOM classification. The model is evaluated using a Landsat-5 image set acquired on a Mexico area before and after two wildfires. As a benchmark, we have considered the classical method of Bayes theory-EM algorithm for selection of pseudo-training sample set combined with a S3VM classifier. The results confirm the effectiveness of our neural approach. Moreover, the exciting adv...