Hierarchical feature exttratction for object recogition in complex SAR image using modified convolutional auto-encoder
Automatic target recognition is a crucial task for SAR remote sensing. Unlike other methods, the unsupervised representation learning based on deep architecture can obtain robust high-level features directly from raw data. A drawback of most unsupervised representation learning methods in SAR ATR is...
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| Vydáno v: | IEEE International Geoscience and Remote Sensing Symposium proceedings s. 854 - 857 |
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| Jazyk: | angličtina |
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IEEE
01.07.2017
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| ISSN: | 2153-7003 |
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| Abstract | Automatic target recognition is a crucial task for SAR remote sensing. Unlike other methods, the unsupervised representation learning based on deep architecture can obtain robust high-level features directly from raw data. A drawback of most unsupervised representation learning methods in SAR ATR is that they only deal with amplitude images. In addition, many methods utlize a single layer architecture to extract pixel-level/mid-level features which are probably sensitive to condition variation. In this paper, a feature extraction method based on modified stacked convolutional denoising auto-encoder (MSCDAE) for complex SAR images is proposed, where convolutional kernels of MSCDAE are learned by 1-D modified denoising auto-encoders. By stacking the convolutional layers and pooling layers, high-level respresntation of objects are learned. The features are subsequently sent to a trained SVM for object classification. Experimetnal results demonstrate that the proposed method can provide a significant improvement in the ATR performance. |
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| AbstractList | Automatic target recognition is a crucial task for SAR remote sensing. Unlike other methods, the unsupervised representation learning based on deep architecture can obtain robust high-level features directly from raw data. A drawback of most unsupervised representation learning methods in SAR ATR is that they only deal with amplitude images. In addition, many methods utlize a single layer architecture to extract pixel-level/mid-level features which are probably sensitive to condition variation. In this paper, a feature extraction method based on modified stacked convolutional denoising auto-encoder (MSCDAE) for complex SAR images is proposed, where convolutional kernels of MSCDAE are learned by 1-D modified denoising auto-encoders. By stacking the convolutional layers and pooling layers, high-level respresntation of objects are learned. The features are subsequently sent to a trained SVM for object classification. Experimetnal results demonstrate that the proposed method can provide a significant improvement in the ATR performance. |
| Author | Zhang, H. Tian, S.R. Wang, C. |
| Author_xml | – sequence: 1 givenname: S.R. surname: Tian fullname: Tian, S.R. organization: Nanjing University of Science and Technology, School of Electronic and Optical Engineering, Department of Electronic Engineering, Nanjing 210094, China – sequence: 2 givenname: C. surname: Wang fullname: Wang, C. organization: Chinese Academy of Sciences, Institute of Remote Sensing and Digital Earth, Beijing 100094, China – sequence: 3 givenname: H. surname: Zhang fullname: Zhang, H. organization: Chinese Academy of Sciences, Institute of Remote Sensing and Digital Earth, Beijing 100094, China |
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| Snippet | Automatic target recognition is a crucial task for SAR remote sensing. Unlike other methods, the unsupervised representation learning based on deep... |
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| SubjectTerms | automatic target recognition(ATR) Deep learning Feature extraction Image recognition Kernel Modified convolutional denoising autoencoder (MCSAE) Noise reduction Radar polarimetry Remote sensing Representation learning Stacking Synthetic aperture radar synthetic aperture radar(SAR) Target recognition |
| Title | Hierarchical feature exttratction for object recogition in complex SAR image using modified convolutional auto-encoder |
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