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Journal of Applied Remote Sensing • new

Deep feature extraction and combination for synthetic aperture radar target classification
Author(s): Moussa Amrani; Feng Jiang
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Paper Abstract

Feature extraction has always been a difficult problem in the classification performance of synthetic aperture radar automatic target recognition (SAR-ATR). It is very important to select discriminative features to train a classifier, which is a prerequisite. Inspired by the great success of convolutional neural network (CNN), we address the problem of SAR target classification by proposing a feature extraction method, which takes advantage of exploiting the extracted deep features from CNNs on SAR images to introduce more powerful discriminative features and robust representation ability for them. First, the pretrained VGG-S net is fine-tuned on moving and stationary target acquisition and recognition (MSTAR) public release database. Second, after a simple preprocessing is performed, the fine-tuned network is used as a fixed feature extractor to extract deep features from the processed SAR images. Third, the extracted deep features are fused by using a traditional concatenation and a discriminant correlation analysis algorithm. Finally, for target classification, K -nearest neighbors algorithm based on LogDet divergence-based metric learning triplet constraints is adopted as a baseline classifier. Experiments on MSTAR are conducted, and the classification accuracy results demonstrate that the proposed method outperforms the state-of-the-art methods.

Paper Details

Date Published: 17 October 2017
PDF: 18 pages
J. Appl. Rem. Sens. 11(4) 042616 doi: 10.1117/1.JRS.11.042616
Published in: Journal of Applied Remote Sensing Volume 11, Issue 4
Show Author Affiliations
Moussa Amrani, Harbin Institute of Technology (China)
Feng Jiang, Harbin Institute of Technology (China)

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