Satellite sensor image classification using cascaded architecture of neural fuzzy network

Satellite sensor images usually contain many complex factors and mixed pixels, so a high classification accuracy is not easy to attain. Especially, for a nonhomogeneous region, gray values of satellite sensor images vary greatly and thus, direct statistic gray values fail to do the categorization task correctly. The goal of this paper is to develop a cascaded architecture of neural fuzzy networks with feature mapping (CNFM) to help the clustering of satellite sensor images. In the CNFM, a Kohonen's self-organizing feature map (SOFM) is used as a preprocessing layer for the reduction of feature domain, which combines original multi-spectral gray values, structural measurements from co-occurrence matrices, and spectrum features from wavelet decomposition. In addition to the benefit of dimensional reduction of feature space, Kohonen's SOFM can remove some noisy areas and prevent the following training process from being overoriented to the training patterns. The condensed measurements are then forwarded into a neural fuzzy network, which performs supervised learning for pattern classification. The proposed cascaded approach is an appropriate technique for handling the classification problem in areas that exhibit large spatial variation and interclass heterogeneity (e.g., urban-rural infringing areas). The CNFM is a general and useful structure that can give us favorable results in terms of classification accuracy and learning speed. Experimental results indicate that our structure can retain high accuracy of classification (90% in average), while the training time is substantially reduced if our system is compared to the commonly used backpropagation network.