SII-Net: Spatial Information Integration Network for Small Target Detection in SAR Images

Article Geochemistry & Geophysics

Enriching SAR Ship Detection via Multistage Domain Alignment

Somi Jeong et al.

Summary: The letter proposes a novel SAR ship detection framework that leverages label-rich electro-optical (EO) images and a multistage domain alignment module to address speckle noise in SAR images, achieving better detection performance.

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS (2022)

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Article Geochemistry & Geophysics

A Lightweight Faster R-CNN for Ship Detection in SAR Images

Yiding Li et al.

Summary: A new and faster region-based convolutional neural network (R-CNN) detection method is proposed in this paper, with a new lightweight network design and the use of K-Means method to optimize the recognition of target scale. The proposed method shows significant improvements in both detection performance and speed.

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS (2022)

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Article Geochemistry & Geophysics

A Novel Multidimensional Domain Deep Learning Network for SAR Ship Detection

Dong Li et al.

Summary: In this paper, a novel multidimensional domain deep learning network for SAR ship detection is proposed to exploit the spatial and frequency-domain complementary features, addressing the unsatisfactory performance of current methods in multiscale, rotation, and complex background scenarios.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2022)

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Article Geochemistry & Geophysics

A Robust One-Stage Detector for Multiscale Ship Detection With Complex Background in Massive SAR Images

Xi Yang et al.

Summary: This article proposes a first-stage ship detector with strong robustness by introducing a coordinate attention module and an increased receptive field module to address the interference and multiscale feature issues in SAR ship detection. Experimental results demonstrate that the proposed method outperforms other state-of-the-art methods.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2022)

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Article Remote Sensing

A regional attention-based detector for SAR ship detection

Xinyue Qi et al.

Summary: The study proposes a single-stage detector based on the attention mechanism, which achieves automatic ship detection in SAR images through pixel-level to box-level segmentation and a top-down feature pyramid structure.

REMOTE SENSING LETTERS (2022)

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Article Remote Sensing

SSS-YOLO: towards more accurate detection for small ships in SAR image

Jingpu Wang et al.

Summary: This paper presents a small-scale ship detection algorithm based on convolutional neural network, focusing on optimizing the detection of small ships in synthetic aperture radar images. The proposed method, evaluated on open datasets, shows superior performance compared to other deep learning models, with a 6.5% improvement in Average Precision and higher detection efficiency than the baseline YOLOv3 model.

REMOTE SENSING LETTERS (2021)

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Article Geochemistry & Geophysics

An Anchor-Free Method Based on Feature Balancing and Refinement Network for Multiscale Ship Detection in SAR Images

Jiamei Fu et al.

Summary: A novel detection method named FBR-Net is proposed in this article, which achieves efficient detection of multiscale SAR ships in complex scenes by eliminating the anchor effect, balancing multiple features, and refining object features.

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING (2021)

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Article Environmental Sciences

N-YOLO: A SAR Ship Detection Using Noise-Classifying and Complete-Target Extraction

Gang Tang et al.

Summary: High-resolution SAR images are increasingly important in ship detection, but challenges remain due to noise and shore image contrasts. The N-YOLO method, incorporating NLC, STPAE, and YOLOv5 modules, shows competitive performance in detecting ships compared to CNN-based algorithms.

REMOTE SENSING (2021)

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Article Environmental Sciences

BiFA-YOLO: A Novel YOLO-Based Method for Arbitrary-Oriented Ship Detection in High-Resolution SAR Images

Zhongzhen Sun et al.

Summary: This article proposes a novel YOLO-based arbitrary-oriented SAR ship detector BiFA-YOLO, which effectively detects arbitrary-oriented and densely arranged ships in HR SAR images through bi-directional feature fusion and angular classification. The method achieves good detection performance in ship detection tasks.

REMOTE SENSING (2021)

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Article Geochemistry & Geophysics

ShipDeNet-20: An Only 20 Convolution Layers and <1-MB Lightweight SAR Ship Detector

Tianwen Zhang et al.

Summary: A lightweight SAR ship detector ShipDeNet-20 with 20 convolution layers and <1 MB model size is proposed to solve the issues of huge network scale and big model size. Experimental results show that ShipDeNet-20 outperforms other state-of-the-art object detectors in terms of accuracy and speed. Additionally, ShipDeNet-20 demonstrates strong migration ability on wide-region SAR images, beneficial for real-time SAR application and future hardware transplantation.

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS (2021)

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Article Environmental Sciences

Quad-FPN: A Novel Quad Feature Pyramid Network for SAR Ship Detection

Tianwen Zhang et al.

Summary: A novel quad feature pyramid network (Quad-FPN) is proposed for ship detection from synthetic aperture radar (SAR) imagery, with extensive ablation studies conducted to confirm its effectiveness. Experiments on five datasets show Quad-FPN's optimal performance compared to other 12 competitive CNN-based SAR ship detectors. Additionally, satisfactory detection results in actual ship detection further demonstrate Quad-FPN's practical application value in marine surveillance.

REMOTE SENSING (2021)

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Article Environmental Sciences

TWC-Net: A SAR Ship Detection Using Two-Way Convolution and Multiscale Feature Mapping

Lei Yu et al.

Summary: This study introduces a novel two-way convolution network (TWC-Net) based on a two-way convolution structure and multiscale feature mapping for processing SAR images. TWC-Net effectively extracts features from SAR images and processes shallow and deep feature information efficiently. The proposed method shows superior performance in ship target detection with a high recall rate, precision, and F-Measure of 93.32%, while having smaller parameters and memory consumption compared to other methods.

REMOTE SENSING (2021)

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Article Engineering, Electrical & Electronic

Multitask Learning for Ship Detection From Synthetic Aperture Radar Images

Xin Zhang et al.

Summary: This study introduces a multitask learning-based object detector (MTL-Det) to improve ship detection performance in SAR images by modeling the problem as three cooperative tasks and utilizing auxiliary subtasks to enhance feature learning. The approach effectively addresses the challenges posed by speckle noise in SAR images and outperforms traditional single-task-based object detectors.

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING (2021)

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Article Engineering, Electrical & Electronic

Boosting Ship Detection in SAR Images With Complementary Pretraining Techniques

Wei Bao et al.

Summary: Deep learning methods have made significant progress in ship detection in SAR images. Pretraining techniques are adopted to support deep neural networks-based SAR ship detectors due to scarce labeled SAR images. However, directly leveraging ImageNet pretraining is hard to obtain a good ship detector because of different imaging perspectives and geometry. Proposed methods, including an optical ship detector (OSD) pretraining technique and an optical-SAR matching (OSM) pretraining technique, aim to address these issues and improve ship detection results. Combining predictions from these two detectors through weighted boxes fusion further enhances detection performance, achieving state-of-the-art results in ship detection in SAR images.

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING (2021)

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Article Environmental Sciences