Vessel Refocusing and Velocity Estimation on SAR Imagery Using the Fractional Fourier Transform

Pelich, Ramona; Longépé, Nicolas; Mercier, Grégoire; Hajduch, Guillaume; Garello, R.

doi:10.1109/tgrs.2015.2487378

Cited by 72 publications

(52 citation statements)

References 33 publications

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“…SAR imaging is widely utilized in stationary target imaging. However, it has a limitation for complex moving targets, especially for nonuniformly rotating ships [5,6,23]. Hence, the SAR imaging result of a nonuniformly rotating ship is usually unfocused.…”

Section: Isar Imaging Model Of the Nonuniformly Rotating Shipmentioning

confidence: 99%

“…We utilized the CPS estimators to obtain the refocused ship images. Here, we utilize the entropy [5,[14][15][16][17]19,20] and contrast [5] to assess image quality in Figure 9. An image with a smaller entropy has better image quality.…”

Section: Nonuniformly Rotating Ship Refocusing With Simulated Datamentioning

confidence: 99%

“…In addition to the self-powered translation, ships also nonuniformly rotate by the influence of sea waves and other factors, which leads to the defocusing of ship images. Many SAR imaging methods [1][2][3][4][5][6] were proposed for moving target refocusing. However, these methods are inapplicable for rotating targets.…”

Section: Introductionmentioning

confidence: 99%

“…Radon-Wigner transform (RWT) [9] and Radon-ambiguity transform (RAT) [10] utilize the Radon transform to detect LFM signals. Fractional Fourier transform (FRFT) [13] is also applied to estimate parameters of LFM signals in SAR imaging [5,6]. For gently rotating ships, the above LFM parameter estimation algorithms can be applied.…”

Section: Introductionmentioning

confidence: 99%

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Nonuniformly-Rotating Ship Refocusing in SAR Imagery Based on the Bilinear Extended Fractional Fourier Transform

Fan

Zhang

2020

Sensors

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Nonuniformly-rotating ship refocusing is very significant in the marine surveillance of satellite synthetic aperture radar (SAR). The majority of ship imaging algorithms is based on the inverse SAR (ISAR) technique. On the basis of the ISAR technique, several parameter estimation algorithms were proposed for nonuniformly rotating ships. But these algorithms still have problems on cross-terms and noise suppression. In this paper, a refocusing algorithm for nonuniformly rotating ships based on the bilinear extended fractional Fourier transform (BEFRFT) is proposed. The ship signal in a range bin can be modeled as a multicomponent cubic phase signal (CPS) after motion compensation. BEFRFT is a bilinear extension of fractional Fourier transform (FRFT), which can estimate the chirp rates and quadratic chirp rates of CPSs. Furthermore, BEFRFT has excellent performances on cross-terms and noise suppression. The results of simulated data and Gaofen-3 data verify the effectiveness of BEFRFT.

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Section: Isar Imaging Model Of the Nonuniformly Rotating Shipmentioning

confidence: 99%

Section: Nonuniformly Rotating Ship Refocusing With Simulated Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Nonuniformly-Rotating Ship Refocusing in SAR Imagery Based on the Bilinear Extended Fractional Fourier Transform

Fan

Zhang

2020

Sensors

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“…As a kind of all-day, all-weather, high-resolution remote sensing techniques, synthetic aperture radar (SAR) becomes one of the main approaches of ship surveillance [1,2]. Currently, researches on ship surveillance are mainly concentrated on target detection and estimation [3][4][5], multichannel sea clutter suppression [6,7], and target refocusing [8,9].…”

Section: Introductionmentioning

confidence: 99%

GRFT-Based Moving Ship Target Detection and Imaging in Geosynchronous SAR

et al. 2018

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Geosynchronous synthetic aperture radar (GEO SAR) has great potentials in ship surveillance due to its high time resolution and wide swath coverage. However, the remote slant range will result in a very low signal-to-noise ratio (SNR) of echoes that need to be enhanced by long-time coherent integration. The generalized Radon-Fourier transform (GRFT) can realize the coherent integration of moving target under long integration time by jointly parameter searching along range and velocity directions. Unfortunately, in GEO SAR, the very large slant range and long synthetic aperture will cause the curved synthetic aperture trajectory and non-negligible signal round-trip delay, leading to the failure of the traditional slant range and GRFT signal model for moving targets. This paper proposes an improved GRFT-based approach to realize the detection and imaging of moving ship targets in GEO SAR. Firstly, the accurate slant range for moving ship targets is constructed and the GRFT signal is redefined considering the curved trajectory and signal round-trip delay in GEO SAR. Then, GRFT responses to different motion parameters are analyzed. The procedures of moving ship targets detection and imaging in GEO SAR are presented through the detection with coarse-searched motion parameters in GRFT and the following imaging with fine-searched motion parameters based on minimum entropy. Finally, computer simulations verify the proposed GRFT-based method. be observed within the range of every 1.5-2.5 h per day [14]. Thus, GEO SAR has great advantages in ship surveillance over vast ocean area.Currently, there are growing interests for GEO SAR, which are mainly concentrated on system design and mission planning [15][16][17][18][19][20][21], imaging algorithms [22][23][24][25], ionospheric effects and compensation [26][27][28], and deformation retrieval [29][30][31]. However, there are rarely related researches on detection and imaging of moving ships.Obviously, due to high time resolution and wide swath coverage, GEO SAR has great potential in monitoring moving ships. However, the remote slant range of GEO SAR will result in a very low signal-to-noise ratio (SNR) of echoes. The defect of low SNR can be compensated for the static target through long time integration [32]. Unfortunately, the parameters of moving targets are unknown, leading to the failure of coherent integration and the resultantly SNR loss. Therefore, traditional detection and imaging methods for moving targets used in LEO SAR and airborne SAR are not suitable for GEO SAR.Moving targets detection and imaging can be divided into single-channel and multi-channel SAR schemes according to SAR system configuration. Because of the limitation of low SNR, multichannel configuration cannot be realized currently, as it is mainly designed for clutter suppression where ships have higher radar cross section (RCS) against the surrounding sea background (in general, ships has higher backscattering energy as the metallic materials and corner reflection structures [33,34]). Therefore, mo...

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Knowledge discovery from remote sensing images: A review

Wang

Yan

et al. 2020

WIREs Data Min & Knowl

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The development of Earth observation (EO) technology has made the volume of remote sensing data archiving continually larger, but the knowledge hidden in massive remote sensing images has not been fully exploited. Through indepth research on the artificial intelligence (AI)-based knowledge discovery approaches from remote sensing images, we divided them into four typical types according to their development stage, including rule-based approaches, data-driven approaches, reinforcement learning approaches, and ensemble methods. The basic principles, typical applications, advantages, and disadvantages have been detailed for commonly used algorithms within each category. Conclusions include the following: (a) Rule-based, data-driven and reinforcement learning algorithms form a trilogy from knowledge to data, and to capabilities. (b) Rule-based data mining algorithms can provide prior knowledge for data-driven approaches, the knowledge discovered by data-driven models can be as an important complement to expert knowledge and rule sets, and reinforcement learning approaches can effectively make up for the lack of training samples or small training sample in data-driven models. (c) The traditional data-driven machine learning approaches and their ensemble methods are the current and may be the future mainstream methods for large regional and even global scale long time series remote sensing data mining and analysis, and improving their computing efficiency is the key research direction.(d) Deep learning, deep reinforcement learning, transfer learning, and an ensemble approach of the three may be the main means for small-area scope, short time series, and key geoscience information extraction from remote sensing images within a long time of the future.

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Vessel Refocusing and Velocity Estimation on SAR Imagery Using the Fractional Fourier Transform

Cited by 72 publications

References 33 publications

Nonuniformly-Rotating Ship Refocusing in SAR Imagery Based on the Bilinear Extended Fractional Fourier Transform

Nonuniformly-Rotating Ship Refocusing in SAR Imagery Based on the Bilinear Extended Fractional Fourier Transform

GRFT-Based Moving Ship Target Detection and Imaging in Geosynchronous SAR

Knowledge discovery from remote sensing images: A review

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