Radar Target Recognition Using Salient Keypoint Descriptors and Multitask Sparse Representation
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Lab-STICC UMR CNRS 6285, ENSTA Bretagne 29806 Brest CEDEX 9, France
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LRIT-CNRST URAC 29, Rabat IT Center, Faculty of Sciences, Mohammed V University, Rabat, BP 1014, Morocco
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LRIT-CNRST, URAC 29, Rabat IT Center, FLSH, Mohammed V University, Rabat, BP 1014, Morocco
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(6), 843; https://doi.org/10.3390/rs10060843
Received: 18 April 2018
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Revised: 23 May 2018
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Accepted: 24 May 2018
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Published: 28 May 2018
(This article belongs to the Special Issue Radar Remote Sensing of Oceans and Coastal Areas)
In this paper, we propose a novel approach to recognize radar targets on inverse synthetic aperture radar (ISAR) and synthetic aperture radar (SAR) images. This approach is based on the multiple salient keypoint descriptors (MSKD) and multitask sparse representation based classification (MSRC). Thus, to characterize the targets in the radar images, we combine the scale-invariant feature transform (SIFT) and the saliency map. The purpose of this combination is to reduce the number of SIFT keypoints by keeping only those located in the target area (salient region); this speeds up the recognition process. After that, we compute the feature vectors of the resulting salient SIFT keypoints (MSKD). This methodology is applied for both training and test images. The MSKD of the training images leads to constructing the dictionary of a sparse convex optimization problem. To achieve the recognition, we adopt the MSRC taking into consideration each vector in the MSKD as a task. This classifier solves the sparse representation problem for each task over the dictionary and determines the class of the radar image according to all sparse reconstruction errors (residuals). The effectiveness of the proposed approach method has been demonstrated by a set of extensive empirical results on ISAR and SAR images databases. The results show the ability of the proposed method to predict adequately the aircraft and the ground targets.
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Keywords:
ATR; ISAR/SAR images; saliency attention; SIFT; multitask-SRC
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MDPI and ACS Style
Karine, A.; Toumi, A.; Khenchaf, A.; El Hassouni, M. Radar Target Recognition Using Salient Keypoint Descriptors and Multitask Sparse Representation. Remote Sens. 2018, 10, 843. https://doi.org/10.3390/rs10060843
AMA Style
Karine A, Toumi A, Khenchaf A, El Hassouni M. Radar Target Recognition Using Salient Keypoint Descriptors and Multitask Sparse Representation. Remote Sensing. 2018; 10(6):843. https://doi.org/10.3390/rs10060843
Chicago/Turabian StyleKarine, Ayoub, Abdelmalek Toumi, Ali Khenchaf, and Mohammed El Hassouni. 2018. "Radar Target Recognition Using Salient Keypoint Descriptors and Multitask Sparse Representation" Remote Sensing 10, no. 6: 843. https://doi.org/10.3390/rs10060843
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