A robust similarity measure for attributed scattering center sets with application to SAR ATR

Ding, Baiyuan; Wen, Gongjian; Zhong, Jing; Ma, Conghui; Yang, Xiu

doi:10.1016/j.neucom.2016.09.007

Cited by 97 publications

(79 citation statements)

References 30 publications

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“…The latter can rely on episodic and semantic features [21] or sparse robust filters [22] that originate from the human cognition process. Other methods include binary operations [23], using the target's scattering centers [15], [24] or the azimuth and range target profiles fusion [25].…”

Section: Introduction Odern Warfare Requires High Performing Autommentioning

confidence: 99%

Fusing Deep Learning and Sparse Coding for SAR ATR

Kechagias‐Stamatis

Aouf

2019

IEEE Trans. Aerosp. Electron. Syst.

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We propose a multi-modal and multi-discipline data fusion strategy appropriate for Automatic Target Recognition (ATR) on Synthetic Aperture Radar imagery. Our architecture fuses a proposed Clustered version of the AlexNet Convolutional Neural Network with Sparse Coding theory that is extended to facilitate an adaptive elastic net optimization concept. Evaluation on the MSTAR dataset yields the highest ATR performance reported yet which is 99.33% and 99.86% for the 3 and 10-class problems respectively.

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Section: Introduction Odern Warfare Requires High Performing Autommentioning

confidence: 99%

Fusing Deep Learning and Sparse Coding for SAR ATR

Kechagias‐Stamatis

Aouf

2019

IEEE Trans. Aerosp. Electron. Syst.

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“…ATR is still one of the most challenging research topics. Many researchers have conducted excellent research on SAR ATR [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

A Vehicle Target Recognition Algorithm for Wide-Angle SAR Based on Joint Feature Set Matching

Peng

2019

Electronics

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Target recognition is an important area in Synthetic Aperture Radar (SAR) research. Wide-angle Synthetic Aperture Radar (WSAR) has obvious advantages in target imaging resolution. This paper presents a vehicle target recognition algorithm for wide-angle SAR, which is based on joint feature set matching (JFSM). In this algorithm, firstly, the modulus stretch step is added in the imaging process of wide-angle SAR to obtain the thinned image of vehicle contour. Secondly, the gravitational-based speckle reduction algorithm is used to obtain a clearer contour image. Thirdly, the image is rotated to obtain a standard orientation image. Subsequently, the image and projection feature sets are extracted. Finally, the JFSM algorithm, which combines the image and projection sets, is used to identify the vehicle model. Experiments show that the recognition accuracy of the proposed algorithm is up to 85%. The proposed algorithm is demonstrated on the Gotcha WSAR dataset.

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“…Principal component analysis (PCA) and linear discriminant analysis (LDA) [2] are usually used for feature extraction from SAR images. Other features, such as geometrical descriptors [3], attributed scattering centers [4,5], and monogenic spectrums [6], are also applied to SAR target recognition. As for the decision engines, various classifiers, including support vector machines (SVM) [7], sparse representation-based classification (SRC) [8,9], and convolutional neural networks (CNN) [10] are employed for target recognition and have achieved delectable results.…”

Section: Introductionmentioning

confidence: 99%

Exploiting Multi-View SAR Images for Robust Target Recognition

Ding

Wen

2017

Remote Sensing

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Abstract:The exploitation of multi-view synthetic aperture radar (SAR) images can effectively improve the performance of target recognition. However, due to the various extended operating conditions (EOCs) in practical applications, some of the collected views may not be discriminative enough for target recognition. Therefore, each of the input views should be examined before being passed through to multi-view recognition. This paper proposes a novel structure for multi-view SAR target recognition. The multi-view images are first classified by sparse representation-based classification (SRC). Based on the output residuals, a reliability level is calculated to evaluate the effectiveness of a certain view for multi-view recognition. Meanwhile, the support samples for each view selected by SRC collaborate to construct an enhanced local dictionary. Then, the selected views are classified by joint sparse representation (JSR) based on the enhanced local dictionary for target recognition. The proposed method can eliminate invalid views for target recognition while enhancing the representation capability of JSR. Therefore, the individual discriminability of each valid view as well as the inner correlation among all of the selected views can be exploited for robust target recognition. Experiments are conducted on the moving and stationary target acquisition recognition (MSTAR) dataset to demonstrate the validity of the proposed method.

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A robust similarity measure for attributed scattering center sets with application to SAR ATR

Cited by 97 publications

References 30 publications

Fusing Deep Learning and Sparse Coding for SAR ATR

Fusing Deep Learning and Sparse Coding for SAR ATR

A Vehicle Target Recognition Algorithm for Wide-Angle SAR Based on Joint Feature Set Matching

Exploiting Multi-View SAR Images for Robust Target Recognition

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