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Journal of Applied Sciences

Year: 2008  |  Volume: 8  |  Issue: 22  |  Page No.: 4088 - 4096

Feature Extraction and Classification of Objects in the Rosette Pattern Using Component Analysis and Neural Network

Hadi Soltanizadeh and Shahriar Baradaran Shokouhi


In this approach, a new method for classification and clustering in the rosette pattern is introduced. In the IR (Infrared) rosette pattern seeker, the obtained IR signal would be sampled and samples in the rosette pattern space would be reconstructed and mapped into a new space. Then samples in the new space are clustered and classified and target class will be detected. Finally, target position would be determined and central gravity of target is computed in order to track the target. The advantage of mapping to this new space which we are named BRMS (Binary Rosette Mapping Space) is that the data in BRMS can be clustered, classified and central gravity computed easily. Process time which is an important parameter in RSIS (Rosette Scanning Infrared Seeker) will be improved compared with previous methods and the effect of the rosette pattern nonlinearity will be decreased. In the next step, features of clusters are extracted by PCA (Principle Component Analysis), ICA (Independent Component Analysis) or LDA (Linear discriminant Analysis), instead of clusters size or intensity in the rosette pattern and then features would be classified by MLP (Multy Layer Perceptron), RBF (Radial Basis Function) and ART (Adaptice Resonance Theory) neural networks and the results will be compared.

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