Chinese Optics Letters, Vol. 3, Issue 1, 0112 (2005)
Morphological self-organizing feature map neural network with applications to automatic target recognition
Shijun Zhang*, Zhongliang Jing, and Jianxun Li
- Institute of Aerospace Information and Control, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200030
The rotation invariant feature of the target is obtained using the multi-direction feature extraction property of the steerable filter. Combining the morphological operation top-hat transform with the self-organizing feature map neural network, the adaptive topological region is selected. Using the erosion operation, the topological region shrinkage is achieved. The steerable filter based morphological self-organizing feature map neural network is applied to automatic target recognition of binary standard patterns and real-world infrared sequence images. Compared with Hamming network and morphological shared-weight networks respectively, the higher recognition correct rate, robust adaptability, quick training, and better generalization of the proposed method are achieved.
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