Adaptive Component Selection-Based Discriminative Model for Object Detection in High-Resolution SAR Imagery

He, Chu; Tu, Mingxia; Xiong, Dehui; Tian, Feng; Liao, Mingsheng

doi:10.3390/ijgi7020072

Cited by 17 publications

(7 citation statements)

References 29 publications

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“…c) Shape and texture-based methods: These methods, which are based on shape and texture, further improve the detection performance. He et al [23], [24] proposed a component-based detection framework in which the component information and probability of detection were combined to eliminate incorrectly detected objects according to the maximum probability principle. Huang et al [25] attempted to enhance the inter-class feature distance between an object and its background using a gray-level co-occurrence matrix.…”

Section: A Traditional Sar Image Object Detectionmentioning

confidence: 99%

Multitask Learning for Ship Detection From Synthetic Aperture Radar Images

Zhang

Huo

et al. 2021

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

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Ship detection from SAR images is inherently subject to the special imaging mechanism of SAR. In recent years, deeplearning-based techniques for detecting objects from optical images have rapidly advanced and promoted the development of SAR image detection technology. However, the strong speckle noise in SAR images degrades low-level feature learning in shallow layers, hindering the higher-level learning of semantic features for object detection. In view of the problems encountered in direct end-to-end feature learning for object detection and the close relationship between objects and auxiliary cues, a multitask learning-based object detector (MTL-Det) is proposed in this paper to distinguish ships in SAR images. The proposed approach models the ship detection problem, not as a single object detection task, but as three cooperative tasks. The model involves two auxiliary sub-tasks that are focused on learning object-specific cues (e.g. texture and shape) for the ship detection task, which is constrained by the pseudo-ground truth generated by the main task. Assisted by auxiliary sub-tasks, the lowlevel features are robust to speckle noise and reliably support high-level feature learning. Compared with traditional singletask-based object detectors, more discriminative object-specific features are learned by multi-task learning without the extra cost of manual labeling. The experiments conducted in this study help demonstrate the advantages of MTL-Det in improving ship detection performance on two SAR datasets: high-resolution SAR images dataset and large-scale SAR ship detection dataset-v1.0.

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Section: A Traditional Sar Image Object Detectionmentioning

confidence: 99%

Multitask Learning for Ship Detection From Synthetic Aperture Radar Images

Zhang

Huo

et al. 2021

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

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“…To improve detection accuracy, some non-CFAR methods were proposed. He et al 6 constructed a histogram of oriented gradient feature pyramid to describe the target from different resolutions. Kaplan 7 employed extended fractal features for SAR object detection.…”

Section: Introductionmentioning

confidence: 99%

Scattering feature extraction and fuse network for aircraft detection in synthetic aperture radar images

Chen

Huang

Lin

2023

J. Appl. Rem. Sens.

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“…Zhang et al proposed a feature-fusion-based ship target-detection algorithm based on a multiscale single-shot detection framework with enhanced network feature extraction by adding deconvolution and pooling feature fusion modules [23]. He et al proposed the Deformable Feature Fusion You Only Look Once (DFF-Yolov5) algorithm based on YOLOv5, which improved the YoloV5 model in two aspects: feature refinement and multifeature fusion in the feature extraction network [24]. Other scholars consider introducing attention mechanisms to solve the above problems [25,26].…”

Section: Introductionmentioning

confidence: 99%

A Novel Deep Learning Network with Deformable Convolution and Attention Mechanisms for Complex Scenes Ship Detection in SAR Images

et al. 2023

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Synthetic aperture radar (SAR) can detect objects in various climate and weather conditions. Therefore, SAR images are widely used for maritime object detection in applications such as maritime transportation safety and fishery law enforcement. However, nearshore ship targets in SAR images are often affected by background clutter, resulting in a low detection rate, high false alarm rate, and high missed detection rate, especially for small-scale ship targets. To address this problem, in this paper, we propose a novel deep learning network with deformable convolution and attention mechanisms to improve the Feature Pyramid Network (FPN) model for nearshore ship target detection in SAR images with complex backgrounds. The proposed model uses a deformable convolutional neural network in the feature extraction network to adapt the convolution position to the target sampling point, enhancing the feature extraction ability of the target, and improving the detection rate of the ship target against the complex background. Moreover, this model uses a channel attention mechanism to capture the feature dependencies between different channel graphs in the feature extraction network and reduce the false detection rate. The designed experiments on a public SAR image ship dataset show that our model achieves 87.9% detection accuracy for complex scenes and 95.1% detection accuracy for small-scale ship targets. A quantitative comparison of the proposed model with several classical and recently developed deep learning models on the same SAR images dataset demonstrated the superior performance of the proposed method over other models.

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Adaptive Component Selection-Based Discriminative Model for Object Detection in High-Resolution SAR Imagery

Cited by 17 publications

References 29 publications

Multitask Learning for Ship Detection From Synthetic Aperture Radar Images

Multitask Learning for Ship Detection From Synthetic Aperture Radar Images

Scattering feature extraction and fuse network for aircraft detection in synthetic aperture radar images

A Novel Deep Learning Network with Deformable Convolution and Attention Mechanisms for Complex Scenes Ship Detection in SAR Images

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