Abstract

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.

© 2005 Chinese Optics Letters

Optical implementation of a feature-based neural network with application to automatic target recognition

Tien-Hsin Chao and William W. Stoner
Appl. Opt. 32(8) 1359-1369 (1993)

Neural-network classifiers for automatic real-world aerial image recognition

Shlomo Greenberg and Hugo Guterman
Appl. Opt. 35(23) 4598-4609 (1996)

High-accuracy automatic target recognition scheme based on a photonic analog-to-digital converter and a convolutional neural network

Jun Wan, Shaofu Xu, and Weiwen Zou
Opt. Lett. 45(24) 6855-6858 (2020)

Assisting target recognition through strong turbulence with the help of neural networks

Kathleen Kee, Chensheng Wu, Daniel A. Paulson, and Christopher C. Davis
Appl. Opt. 59(30) 9434-9442 (2020)

Automatic target detection in forward-looking infrared imagery via probabilistic neural networks

Jesmin F. Khan, Mohammad S. Alam, and Sharif M. A. Bhuiyan
Appl. Opt. 48(3) 464-476 (2009)