Speckle-Noise-Invariant Convolutional Neural Network for SAR Target Recognition

Kwak, Youngchul; Song, Woo‐Jin; Kim, Seong‐Eun

doi:10.1109/lgrs.2018.2877599

Cited by 59 publications

(40 citation statements)

References 14 publications

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“…e speckle component in SAR images is inevitable due to the special imaging mechanism. As a result, for any SAR image, it can be expressed as [18,19] i � A ∘ n, (1) where A represents the radar cross-section coefficients of the clutter, n represents the noise component, and the symbol "∘" represents element-wise multiplication. e product model shown in (1) can give a better description of the SAR image with respect to other models, such as the additive model [18,19].…”

Section: The Proposed Product Dictionary Learning (Pdl) Algorithmmentioning

confidence: 99%

“…e procedures of the proposed method are summarized as follows. (1) e training samples are divided into C subsets according to the label of the samples.…”

Section: The Proposed Product Dictionary Learning (Pdl) Algorithmmentioning

confidence: 99%

“…Step (1) and the computation complexity is O(N Φ c ) [23,28]. As a result, the computation complexity of obtaining all the Configuration T72 T72 T72 T72 T72 T72 T72 T72 T72 T72 T72 132 812 S7 A04 A05 A07 A10 A32 A62 A63 A64 Training dataset (17°) 232 231 228 299 299 299 296 298 299 299 299 Testing dataset (15°) 196 195 191 274 274 274 271 274 274 274 274 132 812 S7 A04 A05 A07 A10 A32 A62 A63 T72 T72 T72 T72 T72 T72 T72 T72 T72 T72 T72 A64 0°8 0°1 60°2 40°F igure 3: SAR images of 11 T72 configurations with different aspect angles under depression angle 17°.…”

Section: Computational Complexity Analysismentioning

confidence: 99%

“…Due to the powerful day and night working ability under inclement weather conditions, the synthetic aperture radar (SAR) has attracted increasing popularity in recent years [1]. As one of the hottest topics related to SAR remote-sensing applications, SAR automatic target recognition (ATR) focuses on the recognition of the interest targets from 2dimensional (2D) high-resolution SAR images.…”

Section: Introductionmentioning

confidence: 99%

“…As for the classifier design, some advanced classifiers such as support vector machine (SVM) [8], sparse representation [7,[9][10][11], and convolutional neural networks (CNN) [1,5,8] have been employed. Algorithms based on CNN [1,5,8] or other deep learning [3] have been enriched enormously. However, due to the special complexity of SAR images and shortage of data amount, these algorithms usually suffer from overfitting and local minima [2].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Product Dictionary Learning-Based SAR Target Configuration Recognition

Liu

Chen

et al. 2020

International Journal of Antennas and Propagation

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Dictionary construction is a key factor for the sparse representation-(SR-) based algorithms. It has been verified that the learned dictionaries are more effective than the predefined ones. In this paper, we propose a product dictionary learning (PDL) algorithm to achieve synthetic aperture radar (SAR) target configuration recognition. e proposed algorithm obtains the dictionaries from a statistical standpoint to enhance the robustness of the proposed algorithm to noise. And, taking the inevitable multiplicative speckle in SAR images into account, the proposed algorithm employs the product model to describe SAR images. A more accurate description of the SAR image results in higher recognition rates. e accuracy and robustness of the proposed algorithm are validated by the moving and stationary target acquisition and recognition (MSTAR) database.

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Section: The Proposed Product Dictionary Learning (Pdl) Algorithmmentioning

confidence: 99%

“…e procedures of the proposed method are summarized as follows. (1) e training samples are divided into C subsets according to the label of the samples.…”

Section: The Proposed Product Dictionary Learning (Pdl) Algorithmmentioning

confidence: 99%