Fuzzy image algebra neural networks for target classification

Rashmi Srivastava; Jennifer L. Davidson

doi:10.1117/12.179186

30 June 1994 Fuzzy image algebra neural networks for target classification

Rashmi Srivastava, Jennifer L. Davidson

Proceedings Volume 2300, Image Algebra and Morphological Image Processing V; (1994) https://doi.org/10.1117/12.179186
Event: SPIE's 1994 International Symposium on Optics, Imaging, and Instrumentation, 1994, San Diego, CA, United States

Abstract

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.

Citation Download Citation

Rashmi Srivastava and Jennifer L. Davidson "Fuzzy image algebra neural networks for target classification", Proc. SPIE 2300, Image Algebra and Morphological Image Processing V, (30 June 1994); https://doi.org/10.1117/12.179186

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