Feature-Transferable Pyramid Network for Dense Multi-Scale Object Detection in SAR Images

Zhou, Zheng; Cui, Zongyong; Cao, Zongjie; Yang, Jianyu

doi:10.1109/igarss46834.2022.9884747

Cited by 6 publications

(5 citation statements)

References 12 publications

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“…However, similar to the traditional methods, CNN-based ship detection algorithms also face the common problem that the detection probability deteriorates in complex environments. To solve this, with the efforts of many scholars, CNNbased methods have achieved significant ship detection performance on high-resolution SAR images [18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

A New Synthetic Aperture Radar Ship Detector Based on Clutter Intensity Statistics in Complex Environments

Liu,

Zhu,

2024

Remote Sensing

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In complex environments, the clutter statistical characteristics of synthetic aperture radar (SAR) are inconstant, and the constant detection performance of a false alarm rate (CFAR) detector based on a clutter statistical model is also hard to achieve. As a result, the overestimated threshold leads to a degradation in detection probability. To this end, this paper proposes a SAR ship detector different from CFAR detectors, which is independent of traditional clutter statistical distribution models and the probability of a false alarm (PFA). The proposed detector aims to raise the ship detection probability and alleviate interference from complex environments such as multi-target areas, shores, and breakwaters. It estimates clutter-truncated thresholds based on clutter intensity statistics (CIS). Firstly, three statistical parameters, including the mean, standard deviation, and maximum intensity of background clutter contaminated by outliers, are calculated; secondly, these parameters are utilized to estimate the clutter-truncated threshold using the novel CIS; and finally, the pixel under test is determined according to the CIS detection rule. Compared with CFAR-based algorithms, CIS obtains a high probability of detection in complex environments. As for other aspects, the CIS detector is insensitive to the structure of the detection window, as well as the size. It is also computationally efficient due to its simple calculations. The superiority of the CIS detector is validated on scene-differed SAR images from the DSSDD dataset.

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Section: Introductionmentioning

confidence: 99%

A New Synthetic Aperture Radar Ship Detector Based on Clutter Intensity Statistics in Complex Environments

Liu,

Zhu,

2024

Remote Sensing

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“…Digital Object Identifier 10.1109/TGRS.2023.3271905 detected [13], [14], [15], [16], [17], [18], [19], [20], [21] and single look complex (SLC) products [5], [6], [7], [8] or at least the range-compressed products [22], [23], [24], [25], [26]. For example, constant false alarm rate (CFAR) [18], [19], [20], [21], [22], [23] is one of the most widely used amplitude-based algorithms that is used to search for unusually brighter pixels than the surrounding sea clutter.…”

Section: Introductionmentioning

confidence: 99%

“…The subaperture and higher order moment detectors can explore the value of using the spectral analysis technique [7] and complex-valued statistical information [5], [6], [8] in the SLC data. Recent methods based on convolutional neural networks (CNNs) [13], [14], [15], [16] can employ nonlinear deep visual features further.…”

Section: Introductionmentioning

confidence: 99%

Ship Detection From Raw SAR Echo Data

Leng

Kuang

2023

IEEE Trans. Geosci. Remote Sensing

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In the context of ship monitoring in the ocean, targets are usually sparsely distributed. Thus, synthetic aperture radar (SAR) imaging of the whole scene is usually quite redundant and costly. However, raw SAR echo data were considered to be useless before focusing. Few studies have attempted to detect ships from raw SAR echo data. It seems to be an impossible task since the resolution of raw SAR echo data is too low. This article proposes a ship detection method for raw SAR echo data in view of a nonimaging target sensing paradigm. The core idea is that we can sense the existence of ships from raw SAR echo data without imaging. The underlying rationale is that the radar always speaks the same sentence, i.e., usually an exactly identical linear frequency modulated (LFM) signal, while target and clutter answer differently. The difference spread into each part of the whole echo sequence rather than only the focused energy after match filtering. Thus, the ships can be found by pattern analysis on one-dimension sequence data rather than twodimension images. The experimental results based on simulation and typical real data validate our assumption. This study shows that SAR imaging is an unnecessary intermediate process and opens up new significant possibilities for ship detection in the vast ocean.

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“…Li [28] et al put forward a feature pyramid network to learn hierarchical spatial features, thereby addressing the problem of multi-scale detection. Zhou [29] put forward a pyramid network with transferable features, which can effectively solve the issue of difficult detection of dense multi-scale targets. Ke [30] put forward a SAR ship detector using a feature enhancement feature pyramid network (FEFPN) to enrich the semantic information of feature maps and using Swin Transformer as the backbone to improve multiscale feature maps.…”

Section: Introductionmentioning

confidence: 99%

Sar Ship Detection Based on Convnext with Multi-Pooling Channel Attention and Feature Intensification Pyramid Network

Wei,

Wang

2023

Sensors

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The advancements in ship detection technology using convolutional neural networks (CNNs) regarding synthetic aperture radar (SAR) images have been significant. Yet, there are still some limitations in the existing detection algorithms. First, the backbones cannot generate high-quality multiscale feature maps. Second, there is a lack of suitable attention mechanisms to suppress false alarms. Third, the current feature intensification algorithms are unable to effectively enhance the shallow feature’s semantic information, which hinders the detection of small ships. Fourth, top-level feature maps have rich semantic information; however, as a result of the reduction of channels, the semantic information is weakened. These four problems lead to poor performance in SAR ship detection and recognition. To address the mentioned issues, we put forward a new approach that has the following characteristics. First, we use Convnext as the backbone to generate high-quality multiscale feature maps. Second, to suppress false alarms, the multi-pooling channel attention (MPCA) is designed to generate a corresponding weight for each channel, suppressing redundant feature maps, and further optimizing the feature maps generated by Convnext. Third, a feature intensification pyramid network (FIPN) is specifically designed to intensify the feature maps, especially the shallow feature maps. Fourth, a top-level feature intensification (TLFI) is also proposed to compensate for semantic information loss within the top-level feature maps by utilizing semantic information from different spaces. The experimental dataset employed is the SAR Ship Detection Dataset (SSDD), and the experimental findings display that our approach exhibits superiority compared to other advanced approaches. The overall Average Precision (AP) reaches up to 95.6% on the SSDD, which improves the accuracy by at least 1.7% compared to the current excellent methods.

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Feature-Transferable Pyramid Network for Dense Multi-Scale Object Detection in SAR Images

Cited by 6 publications

References 12 publications

A New Synthetic Aperture Radar Ship Detector Based on Clutter Intensity Statistics in Complex Environments

A New Synthetic Aperture Radar Ship Detector Based on Clutter Intensity Statistics in Complex Environments

Ship Detection From Raw SAR Echo Data

Sar Ship Detection Based on Convnext with Multi-Pooling Channel Attention and Feature Intensification Pyramid Network

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