Development and evaluation of a real time SAR ATR system

Greenspan, M.; Pham, Laurie; Tardella, Neil

doi:10.1109/nrc.1998.677974

Cited by 8 publications

(2 citation statements)

References 2 publications

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“…With the enhancement of SAR data acquisition capability, Synthetic Aperture Radar Automatic Target Recognition (SAR ATR) [3] has become a key technology and research hotspot of radar signal processing. Traditional SAR image recognition methods, such as template matching [4], feature-based approaches [5,6], and CAD model-based methods [7], predominantly rely on the statistical and physical characteristics inherent in the image data. This methodology offers robust interpretability, as the identified features and models possess well-defined statistical or physical interpretations.…”

Section: Introductionmentioning

confidence: 99%

C-RISE: A Post-Hoc Interpretation Method of Black-Box Models for SAR ATR

et al. 2023

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The integration of deep learning methods, especially Convolutional Neural Networks (CNN), and Synthetic Aperture Radar Automatic Target Recognition (SAR ATR) has been widely deployed in the field of radar signal processing. Nevertheless, these methods are frequently regarded as black-box models due to the limited visual interpretation of their internal feature representation and parameter organization. In this paper, we propose an innovative approach named C-RISE, which builds upon the RISE algorithm to provide a post-hoc interpretation technique for black-box models used in SAR Images Target Recognition. C-RISE generates saliency maps that effectively visualize the significance of each pixel. Our algorithm outperforms RISE by clustering masks that capture similar fusion features into distinct groups, enabling more appropriate weight distribution and increased focus on the target area. Furthermore, we employ Gaussian blur to process the masked area, preserving the original image structure with optimal consistency and integrity. C-RISE has been extensively evaluated through experiments, and the results demonstrate superior performance over other interpretation methods based on perturbation when applied to neural networks for SAR image target recognition. Furthermore, our approach is highly robust and transferable compared to other interpretable algorithms, including white-box methods.

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Section: Introductionmentioning

confidence: 99%

C-RISE: A Post-Hoc Interpretation Method of Black-Box Models for SAR ATR

et al. 2023

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“…The detection of ship targets in SAR images and the acquisition of the ship position, track, and other information are of great significance to many applications, such as maritime rescue, marine traffic management, and military intelligence collection. Ship detection based on SAR images is the first stage of the maritime ship target detection system, and it is also the basis of ship identification [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

A Novel Method for SAR Ship Detection Based on Eigensubspace Projection

Shu

Chang

et al. 2022

Remote Sensing

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Synthetic Aperture Radar (SAR) is a high-resolution radar that operates all day and in all weather conditions, so it has been widely used in various fields of science and technology. Ship detection using SAR images has become important research in marine applications. However, in complex scenes, ships are easily submerged in sea clutter, which cause missed detection. Due to this, strong sidelobes in SAR images generate false targets and reduce the detection accuracy. To solve these problems, a ship detection method based on eigensubspace projection (ESSP) in SAR images is proposed. First, the image is reconstructed into a new observation matrix along the azimuth direction, and the phase space matrix of the reconstructed image is constructed by using the Hankel characteristic, which preliminarily determines the approximate position of the ship. Then, the autocorrelation matrix of the reconstructed image is decomposed by eigenvalue decomposition (EVD). According to the size of the eigenvalues, the corresponding eigenvectors are divided into two parts, which constitute the basis of the ship subspace and the clutter subspace. Finally, the original image is projected into the ship subspace, and the ship data in the ship subspace are rearranged to obtain the precise position of the ship with significantly suppressed clutter. To verify the effectiveness of the proposed method, the ESSP method is compared with other detection methods on four images at different sea conditions. The results show that the detection accuracy of the ESSP method reaches 89.87% in complex scenes. Compared with other methods, the proposed method can extract ship targets from sea clutter more accurately and reduce the number of false alarms, which has obvious advantages in terms of detection accuracy and timeliness.

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A fast line-scanning-based detection algorithm for real-time SAR ship detection

Wang

Chen

2014

SIViP

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Development and evaluation of a real time SAR ATR system

Cited by 8 publications

References 2 publications

C-RISE: A Post-Hoc Interpretation Method of Black-Box Models for SAR ATR

C-RISE: A Post-Hoc Interpretation Method of Black-Box Models for SAR ATR

A Novel Method for SAR Ship Detection Based on Eigensubspace Projection

A fast line-scanning-based detection algorithm for real-time SAR ship detection

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