Neuro-fuzzy Based Analysis of Hyperspectral Imagery

A neuro-fuzzy system, namely Gaussian Fuzzy Learning Vector Quantization (GFLVQ), was developed based on the synergy of a neural network and a fuzzy system. GFLVQ is both a fuzzy neural network and a neural fuzzy system with supervised learning and unsupervised self-organizing capabilities. In this paper, GFLVQ was further improved to efficiently and effectively process hyperspectral data through training data informed initialization and a simplified fuzzy learning algorithm. A geovisualization tool was developed to facilitate knowledge discovery and understanding of the hyperspectral image. A case study was conducted using a Hyperion image. The results obtained from the improved neuro-fuzzy system were found to be significantly better than those from conventional statistics-based and endmember-based classifiers. The fuzzy spectral profiles produced from the geovisualization tool provided an extra insight into the neuro-fuzzy learning process, further opening up the black box of the neural network.