A scale transfer convolution network for small ship detection in SAR images

Gui, Yunchuan; Li, Xiuhe; Xue, Lei; Lv, Jijie

doi:10.1109/itaic.2019.8785805

Cited by 8 publications

(4 citation statements)

References 12 publications

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“…Therefore, the dense scale-transfer connection could expand the resolution of feature maps and explicitly explore valuable information contained in channels. A scale transfer module was also used in [484] to connect with several feature maps to extract multiscale features for STD. In addition, RoIAlign was adapted to calibrate the accuracy of the bounding boxes, and the context features were employed to assist the detection of complex targets in detection subnetwork.…”

Section: I) Improving Accurately Location Of Ship Targetsmentioning

confidence: 99%

A Comprehensive Survey of Machine Learning Applied to Radar Signal Processing

Lang,

Fu,

Martorella

et al. 2020

Preprint

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Modern radar systems have high requirements in terms of accuracy, robustness and real-time capability when operating on increasingly complex electromagnetic environments. Traditional radar signal processing (RSP) methods have shown some limitations when meeting such requirements, particularly in matters of target classification. With the rapid development of machine learning (ML), especially deep learning, radar researchers have started integrating these new methods when solving RSP-related problems. This paper aims at helping researchers and practitioners to better understand the application of ML techniques to RSP-related problems by providing a comprehensive, structured and reasoned literature overview of ML-based RSP techniques. This work is amply introduced by providing general elements of ML-based RSP and by stating the motivations behind them. The main applications of ML-based RSP are then analysed and structured based on the application field. This paper then concludes with a series of open questions and proposed research directions, in order to indicate current gaps and potential future solutions and trends.

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Section: I) Improving Accurately Location Of Ship Targetsmentioning

confidence: 99%

A Comprehensive Survey of Machine Learning Applied to Radar Signal Processing

Lang,

Fu,

Martorella

et al. 2020

Preprint

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show abstract

“…In particular, Ref. [6] attempted to develop a DL algorithm for detecting small targets as an experimental challenge. However, the results appeared to have limited applicability due to the insufficient quantity and quality of the dataset.…”

Section: Introductionmentioning

confidence: 99%

Enhancement of Small Ship Detection Using Polarimetric Combination from Sentinel−1 Imagery

Shin,

Yang,

Chowdhury

2024

Remote Sensing

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Speckle noise and the spatial resolution of the Sentinel−1 Synthetic Aperture Radar (SAR) image can cause significant difficulties in the detection of small objects, such as small ships. Therefore, in this study, the Polarimetric Combination-based Ship Detection (PCSD) approach is proposed for enhancing small ship detection performance, which combines three different characteristics of polarization: newVH, enhanced VH, and enhanced VV. Employing the Radar Cross Section (RCS) value in three stages, the newVH was utilized to detect Automatic Identification System (AIS) -ships and small ships. In the first step, the adaptive threshold (AT) method was applied to newVH with a high RCS condition (>−10.36 (dB)) for detecting AIS-ships. Secondly, the first small ship target was detected with the maximum suppression of false alarms by using the AT with a middle RCS condition (>−16.98 (dB)). In the third step, a candidate group was identified by applying a condition to the RCS values (>−23.01 (dB)), where both small ships and speckle noise were present simultaneously. Subsequently, the enhanced VH and VV polarizations were employed, and an optimized threshold value was selected for each polarization to detect the second small ship while eliminating noise pixels. Finally, the results were evaluated using the AIS and small fishing vessel tracking system (V-Pass) based on the detected ship positions and ship lengths. The average matching results from 26 scenes in 2022 indicated a matching rate of over 86.67% for AIS-ships. Regarding small ships, the detection performance of PCSD was 42.27%, which was over twice as accurate as the previous Constant False Alarm Rate (CFAR) ship detection model. As a result, PCSD enhanced the detection rate of small ships while maintaining the capacity for detecting AIS-equipped ships.

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“…However, as the complexity of the clutter model increases, the parameter estimation becomes more arduous, thus the computation burden grows. What's more, CFAR based methods have limitations in detecting ships in complex environments [7], such as harbors and coastal waters since the scattering features of ship is similar to that of the strong onshore scatterers, leading to high probability of false alarms. An optional strategy is to mask the land using the sea-land segmentation before the CFAR process.…”

Section: Introductionmentioning

confidence: 99%

Ship Detection in Complex Environment Using SAR Time Series

Kahar

2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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A scale transfer convolution network for small ship detection in SAR images

Cited by 8 publications

References 12 publications

A Comprehensive Survey of Machine Learning Applied to Radar Signal Processing

A Comprehensive Survey of Machine Learning Applied to Radar Signal Processing

Enhancement of Small Ship Detection Using Polarimetric Combination from Sentinel−1 Imagery

Ship Detection in Complex Environment Using SAR Time Series

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