Radar Target Recognition Using Salient Keypoint Descriptors and Multitask Sparse Representation

Karine, Ayoub; Toumi, Abdelmalek; Khenchaf, Ali; Hassouni, Mohammed El

doi:10.3390/rs10060843

Cited by 27 publications

(23 citation statements)

References 38 publications

(11 reference statements)

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“…After extracting keypoints of SIFT, we will extract harbor candidates. It is a common method to use SIFT keypoints to match template images and target images [28], so we will use this method. Because the breakwaters of harbors are semi-closed and are also prominent in the sea, we use the breakwaters as the main keypoints of matching.…”

Section: Harbor Candidate Extractionmentioning

confidence: 99%

Harbor Extraction Based on Edge-Preserve and Edge Categories in High Spatial Resolution Remote-Sensing Images

Guo

Zhang

et al. 2019

Applied Sciences

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Efficient harbor extraction is essential due to the strategic importance of this target in economic and military construction. However, there are few studies on harbor extraction. In this article, a new harbor extraction algorithm based on edge preservation and edge categories (EC) is proposed for high spatial resolution remote-sensing images. In the preprocessing stage, we propose a local edge preservation algorithm (LEPA) to remove redundant details and reduce useless edges. After acquiring the local edge-preserve images, in order to reduce the redundant matched keypoints and improve the accuracy of the target candidate extraction method, we propose a scale-invariant feature transform (SIFT) keypoints extraction method based on edge categories (EC-SIFT): this method greatly reduces the redundancy of SIFT keypoint and improves the computational complexity of the target extraction system. Finally, the harbor extraction algorithm uses the Support Vector Machine (SVM) classifier to identify the harbor target. The experimental results show that the proposed algorithm effectively removes redundant details and improves the accuracy and efficiency of harbor target extraction.

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Section: Harbor Candidate Extractionmentioning

confidence: 99%

Harbor Extraction Based on Edge-Preserve and Edge Categories in High Spatial Resolution Remote-Sensing Images

Guo

Zhang

et al. 2019

Applied Sciences

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“…In this paper, the training dataset and test dataset are generated from the MSTAR dataset, provided by the Air Force Research Laboratory and the Defence Advanced Research Projects Agency (AFRL/DARPA) [4]. The dataset serves as a standard data set for the research of SAR ATR.…”

Section: Dataset Generationmentioning

confidence: 99%

“…Automatic target recognition (ATR) is the process of automatic target acquisition and classification, which is capable of recognizing targets or other objects, based on data obtained from the sensors, which has good application prospects in both military and civilian areas [3]. The process of SAR ATR can be summarized as finding regions of interest (ROIs) in the observed SAR image and classifying the category of each ROI (e.g., T72 or BTR70) [4]. Some earlier methods of SAR ATR can be found in [5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

D-ATR for SAR Images Based on Deep Neural Networks

et al. 2019

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Automatic target recognition (ATR) can obtain important information for target surveillance from Synthetic Aperture Radar (SAR) images. Thus, a direct automatic target recognition (D-ATR) method, based on a deep neural network (DNN), is proposed in this paper. To recognize targets in large-scene SAR images, the traditional methods of SAR ATR are comprised of four major steps: detection, discrimination, feature extraction, and classification. However, the recognition performance is sensitive to each step, as the processing result from each step will affect the following step. Meanwhile, these processes are independent, which means that there is still room for processing speed improvement. The proposed D-ATR method can integrate these steps as a whole system and directly recognize targets in large-scene SAR images, by encapsulating all of the computation in a single deep convolutional neural network (DCNN). Before the DCNN, a fast sliding method is proposed to partition the large image into sub-images, to avoid information loss when resizing the input images, and to avoid the target being divided into several parts. After the DCNN, non-maximum suppression between sub-images (NMSS) is performed on the results of the sub-images, to obtain an accurate result of the large-scene SAR image. Experiments on the MSTAR dataset and large-scene SAR images (with resolution 1478 × 1784) show that the proposed method can obtain a high accuracy and fast processing speed, and out-performs other methods, such as CFAR+SVM, Region-based CNN, and YOLOv2.

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“…If performing variable sampling or intensive sampling, then a more accurate signal can be obtained, but intensive sampling generates a lot of data. In data compression, signal and image feature extraction processing of other fields, they provide us some inspiration for processing MRS signals, such as synthetic aperture radar (SAR) images [27], penetrating radar noise filter [28], image processing [29] and so on. In particular, data compression and high-frequency sampling in SAR equipment provide us with exploring direction to process noisy MRS signal.…”

Section: Introductionmentioning

confidence: 99%

Random Noise Suppression of Magnetic Resonance Sounding Data with Intensive Sampling Sparse Reconstruction and Kernel Regression Estimation

et al. 2019

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The magnetic resonance sounding (MRS) method is a non-invasive, efficient and advanced geophysical method for groundwater detection. However, the MRS signal received by the coil sensor is extremely susceptible to electromagnetic noise interference. In MRS data processing, random noise suppression of noisy MRS data is an important research aspect. We propose an approach for intensive sampling sparse reconstruction (ISSR) and kernel regression estimation (KRE) to suppress random noise. The approach is based on variable frequency sampling, numerical integration and statistical signal processing combined with kernel regression estimation. In order to realize the approach, we proposed three specific sparse reconstructions, namely rectangular sparse reconstruction, trapezoidal sparse reconstruction and Simpson sparse reconstruction. To solve the distortion of peaks and valleys after sparse reconstruction, we introduced the KRE to deal with the processed data by the ISSR. Further, the simulation and field experiments demonstrate that the ISSR-KRE approach is a feasible and effective way to suppress random noise. Besides, we find that rectangular sparse reconstruction and trapezoidal sparse reconstruction are superior to Simpson sparse reconstruction in terms of noise suppression effect, and sampling frequency is positively correlated with signal-to-noise improvement ratio (SNIR). In one case of field experiment, the standard deviation of noisy MRS data was reduced from 1200.80 nV to 570.01 nV by the ISSR-KRE approach. The proposed approach provides theoretical support for random noise suppression and contributes to the development of MRS instrument with low power consumption and high efficiency. In the future, we will integrate the approach into MRS instrument and attempt to utilize them to eliminate harmonic noise from power line.

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Radar Target Recognition Using Salient Keypoint Descriptors and Multitask Sparse Representation

Cited by 27 publications

References 38 publications

Harbor Extraction Based on Edge-Preserve and Edge Categories in High Spatial Resolution Remote-Sensing Images

Harbor Extraction Based on Edge-Preserve and Edge Categories in High Spatial Resolution Remote-Sensing Images

D-ATR for SAR Images Based on Deep Neural Networks

Random Noise Suppression of Magnetic Resonance Sounding Data with Intensive Sampling Sparse Reconstruction and Kernel Regression Estimation

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