Paper Title:
Aircraft Type Recognition in High-Resolution SAR Images Using Multi-Scale Autoconvolution
  Abstract

Automatic target recognition is the key stage of SAR image interpretation system and has been taking a great interest to the researchers in recent years. Aiming at the issue of aircraft type recognition in high-resolution SAR images, a novel method based on multi-scale autoconvolution (MSA) affine invariant moment is proposed. First, the texture analysis and clustering method are used to segment the SAR images and then the denoising algorithm and morphological processing are applied to segmented results. Second, 29 MSA features are extracted and form a feature vector to represent the target, then the vector components are standardized by gauss normalization. In the final, the vectors are classified by using the nearest neighbor classifier and template library constructed previously. Experimental results show that the proposed method can obtain high accuracy rate with high processing speed, in which the accuracy rate of two type aircrafts with real data arrives at 85.17% and the accuracy rate of four type aircrafts with simulated data arrives at 87.85%.

  Info
Periodical
Key Engineering Materials (Volumes 439-440)
Edited by
Yanwen Wu
Pages
1475-1480
DOI
10.4028/www.scientific.net/KEM.439-440.1475
Citation
L. P. Zhang, C. Wang, H. Zhang, B. Zhang, "Aircraft Type Recognition in High-Resolution SAR Images Using Multi-Scale Autoconvolution", Key Engineering Materials, Vols. 439-440, pp. 1475-1480, 2010
Online since
June 2010
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Price
$32.00
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