Target recognition from ISAR image using polar mapping and shape matrix

Cexus, Jean-Christophe; Toumi, Abdelmalek; Riahi, Maroua

doi:10.1109/atsip49331.2020.9231528

Cited by 6 publications

(2 citation statements)

References 24 publications

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“…Automatic Target Recognition (ATR) using ISAR images has grown in recent years [1][2][3][4][5]. Most of them trained classifiers for aircraft and space target recognition, which requires many samples.…”

Section: Introductionmentioning

confidence: 99%

Ship target recognition based on superstructure matching

Deng,

2024

E3S Web Conf.

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The Automatic Target Recognition(ATR) of ship targets based on Inverse Synthetic Aperture Radar(ISAR) images plays a significant role in naval warfare.To address the issue that existing superstructure-based recognition methods do not fully use superstructure shape information,we propose a novel automatic ship recognition method based on superstructure matching using the Dynamic Time Warping (DTW) algorithm.Firstly,the superstructures of each class are extracted from ISAR images after preprocessing and preliminary calibration,then the DTW algorithm is used to create the superstructure templates, and eventually the target's class is determined by the DTW distance from the templates.The proposed method is robust to ship deformation from changing aspect angles and requires few training samples per class.The experimental results of applying the technique on real ISAR data demonstrate the effectiveness of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Ship target recognition based on superstructure matching

Deng,

2024

E3S Web Conf.

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“…The recognition methods for ISAR images can be categorized into three groups: the neural network-based [7][8][9][10][11][12], transform domain-based [13][14][15][16][17][18][19][20][21][22][23], and geometrical featuresbased [24][25][26][27][28] methods. These recognition methods are all studied for the attitude change.…”

Section: Introductionmentioning

confidence: 99%

Automatic ISAR Ship Detection Using Triangle-Points Affine Transform Reconstruction Algorithm

Jin

et al. 2023

Remote Sensing

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With the capability of capturing a target’s two-dimensional information, Inverse Synthetic Aperture Radar (ISAR) imaging is widely used in Radar Automatic Target Recognition. However, changes in the ship target’s attitude can lead to the scatterers’ rotation, occlusion, and angle glint, reducing the accuracy of ISAR image recognition. To solve this problem, we proposed a Triangle Preserving level-set-assisted Triangle-Points Affine Transform Reconstruction (TP-TATR) for ISAR ship target recognition. Firstly, three geometric points as initial information were extracted from the preprocessed ISAR images based on the ship features. Combined with these points, the Triangle Preserving level-set (TP) method robustly extracted the fitting triangle of targets depending on the intrinsic structure of the ship target. Based on the extracted triangle, the TP-TATR adjusted all the ship targets from the training and test data to the same attitude, thereby alleviating the attitude sensitivity. Finally, we created templates by averaging the adjusted training data and matched the test data with the templates for recognition. Experiments based on the simulated and measured data indicate that the accuracies of the TP-TATR method are 87.70% and 90.03%, respectively, which are higher than those of the contrast algorithms and have a statistical difference. These demonstrate the effectiveness and robustness of our proposed TP-TATR method.

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A Target Recognition Algorithm of Multi-Source Remote Sensing Image Based on Visual Internet of Things

Sun

Lin

2022

Mobile Netw Appl

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Multi-source remote sensing images have the characteristics of large differences in texture and gray level. Mismatch and low recognition accuracy are easy to occur in the process of identifying targets. Thus, in this paper, the target recognition algorithm of multi-source remote sensing image based on IoT vision is investigated. The infrared sensor and SAR radars are set in the visual perception layer of the iVIOT. The visual perception layer transmits the collected remote sensing image information to the application layer through the wireless networks. The data processing module in the application layer uses the normalized central moment idea to extract the features of multi-source remote sensing image. Contourlet two-level decomposition is performed on the image after feature extraction to realize multi-scale and multi-directional feature fusion. A two-step method of primary fineness is used to match the fused features and the random sampling consensus algorithm is used to eliminate false matches for obtaining the correct match pairs. After the image feature matching is completed, the BVM target detection operator is used to complete the target recognition of multi-source remote sensing image. Experimental results show that the use of the IoT to visually recognizing the desired remote sensing image target has low communication overhead, and the recognition reaches 99% accuracy.

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Target recognition from ISAR image using polar mapping and shape matrix

Cited by 6 publications

References 24 publications

Ship target recognition based on superstructure matching

Ship target recognition based on superstructure matching

Automatic ISAR Ship Detection Using Triangle-Points Affine Transform Reconstruction Algorithm

A Target Recognition Algorithm of Multi-Source Remote Sensing Image Based on Visual Internet of Things

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