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 advantage of the proposed model over the classical ones is that it does not require any statistical assumptions regarding changed/unchanged SCVs data and it implies a reduced computational effort.