Next Article in Journal
Staring Spotlight TerraSAR-X SAR Interferometry for Identification and Monitoring of Small-Scale Landslide Deformation
Next Article in Special Issue
Short-Term Forecasting of Coastal Surface Currents Using High Frequency Radar Data and Artificial Neural Networks
Previous Article in Journal
Classifying the Built-Up Structure of Urban Blocks with Probabilistic Graphical Models and TerraSAR-X Spotlight Imagery
Previous Article in Special Issue
Evaluation of ISS-RapidScat Wind Vectors Using Buoys and ASCAT Data
Open AccessArticle

Radar Target Recognition Using Salient Keypoint Descriptors and Multitask Sparse Representation

1
Lab-STICC UMR CNRS 6285, ENSTA Bretagne 29806 Brest CEDEX 9, France
2
LRIT-CNRST URAC 29, Rabat IT Center, Faculty of Sciences, Mohammed V University, Rabat, BP 1014, Morocco
3
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 / Revised: 23 May 2018 / Accepted: 24 May 2018 / 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. View Full-Text
Keywords: ATR; ISAR/SAR images; saliency attention; SIFT; multitask-SRC ATR; ISAR/SAR images; saliency attention; SIFT; multitask-SRC
Show Figures

Graphical abstract

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.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop