Fuzzy image algebra neural networks for target classification
Rashmi Srivastava, Jennifer L. Davidson · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1994
This paper presents a neural network application to target classification using a new type of neural network called the Fuzzy Image Algebra Neural Network (FIANN). The FIANN is used in a heterogenous network structure. The first layer of the net performs feature extraction, while the remaining layers are used for classification. Generalized image algebra operations are used to obtain fuzzy morphological or linear operation. The parameters for the generalized operations are learned in a fashion similar to standard backpropagation, but with training rules based on a combination of stochastic learning and gradient descent techniques. The type of data used is the range data part of tank LADAR data. The objective is to classify the tanks by type. The range data is first converted to elevation data, which is input to the net for feature extraction and classification. A two tiered approach is used for training. The first layer learns image features, while the top layers perform classification.