Optimizing Kernel PCA Using Sparse Representation-Based Classifier for MSTAR SAR Image Target Recognition

Chen, Lin; Wang, Binghui; Zhao, Xuefeng; Pang, Meng

doi:10.1155/2013/847062

Cited by 10 publications

(7 citation statements)

References 21 publications

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“…SAR-ATR systems generally consist of three stages: detection [1,2], recognition, and classification. At present, common methods of SAR-ATR include template matching [3], support vector machine (SVM) [4], Linear interpolation [5] Principal Component Analysis [6,7], multi-modal dictionary learning and sparse representation combined [8,9], etc. These methods have been successful in some way, but they rely heavily on experience of experts.…”

Section: Introductionmentioning

confidence: 99%

A New Algorithm for SAR Image Target Recognition Based on an Improved Deep Convolutional Neural Network

et al. 2018

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Background: To effectively make use of deep learning technology automatic feature extraction ability, and enhance the ability of depth learning method to learn and recognize features, this paper proposed a deep learning algorithm combining Deep Convolutional Neural Network (DCNN) trained with an improved cost function and Support Vector Machine (SVM). Methods: The class separation information, which explicitly facilitates intra-class compactness and interclass separability in the process of learning features, is added to an improved cost function as a regularization term to enhance the feature extraction ability of DCNN. Then the improved DCNN is applied to learn the features of SAR images. Finally, SVM is utilized to map the features into output labels. Results: Experiments are performed on SAR image data in Moving and Stationary Target Acquisition and Recognition (MSTAR) database. The experiment results prove the effectiveness of our method, achieving an average accuracy of 99% on ten types of targets, some variants, and some articulated targets. Conclusion: It proves that our method is effective and CNN enjoys a certain potential to be applied in SAR image target recognition. Keywords synthetic aperture radar (SAR) images • automatic target recognition (ATR) • deep convolutional neural Network (DCNN) • support vector machine (SVM) • class separation information

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Section: Introductionmentioning

confidence: 99%

A New Algorithm for SAR Image Target Recognition Based on an Improved Deep Convolutional Neural Network

et al. 2018

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“…The ROIs are sent to the classifier to decide the target class [ 10 , 11 ]. In the literature, the automatic SAR target recognition technology generally includes the traditional template matching method, the model-based algorithm, and the methods based on features such as principal component analysis (PCA) [ 12 , 13 ], wavelet transform [ 14 , 15 ], radon transform [ 16 ]. In addition, considering the scattering characteristics of SAR images, there are two typical models: conditionally Gaussian model [ 11 ] and scattering centers model [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Local Aspect Dictionary Pair Learning for Synthetic Aperture Radar Target Image Classification

Zhang

Tan

Liu

et al. 2018

Sensors

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In this paper, a new target classification algorithm based on adaptive local aspect dictionary pair learning for synthetic aperture radar (SAR) images is developed. To that end, first, the aspect sector of one testing sample is determined adaptively by a regularized non-negative sparse learning method. Second, a synthesis dictionary and an analysis dictionary are jointly learned from the corresponding training subset located in the aspect sector. By doing so, the local aspect dictionary pair is obtained. Finally, the class label of the testing sample is inferred by a use of the minimum reconstruction residual under the representation with the local aspect dictionary pair. Using the local aspect sector training subset rather than the global aspect training set reduces the interference of a large amount of unrelated training samples, which leads to a more discriminative local aspect dictionary pair for target classification. The experiments are conducted with the Moving and Stationary Target Acquisition and Recognition (MSTAR) database, and the results demonstrate that the proposed approach is effective and superior to the state-of-the-art methods.

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“…Pang and Yuan [12] proposed to substitute L2-norm with L1-norm to improve the robustness of LPP against outliers. Although SKLPP showed good performance in [7], the selection of kernel function has a significant influence on the kernel feature extraction and the problem was widely studied in the previous works [7,[13][14][15]. In [15], we proposed a Kernel Optimized PCA (KOPCA) with sparse representation-based classifier (SRC).…”

Section: Introductionmentioning

confidence: 99%

Supervised Kernel Optimized Locality Preserving Projection with Its Application to Face Recognition and Palm Biometrics

Chen¹,

Jiang²,

Zhao

et al. 2015

Mathematical Problems in Engineering

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Kernel Locality Preserving Projection (KLPP) algorithm can effectively preserve the neighborhood structure of the database using the kernel trick. We have known that supervised KLPP (SKLPP) can preserve within-class geometric structures by using label information. However, the conventional SKLPP algorithm endures the kernel selection which has significant impact on the performances of SKLPP. In order to overcome this limitation, a method named supervised kernel optimized LPP (SKOLPP) is proposed in this paper, which can maximize the class separability in kernel learning. The proposed method maps the data from the original space to a higher dimensional kernel space using a data-dependent kernel. The adaptive parameters of the data-dependent kernel are automatically calculated through optimizing an objective function. Consequently, the nonlinear features extracted by SKOLPP have larger discriminative ability compared with SKLPP and are more adaptive to the input data. Experimental results on ORL, Yale, AR, and Palmprint databases showed the effectiveness of the proposed method.

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Optimizing Kernel PCA Using Sparse Representation-Based Classifier for MSTAR SAR Image Target Recognition

Cited by 10 publications

References 21 publications

A New Algorithm for SAR Image Target Recognition Based on an Improved Deep Convolutional Neural Network

A New Algorithm for SAR Image Target Recognition Based on an Improved Deep Convolutional Neural Network

Adaptive Local Aspect Dictionary Pair Learning for Synthetic Aperture Radar Target Image Classification

Supervised Kernel Optimized Locality Preserving Projection with Its Application to Face Recognition and Palm Biometrics

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