Proposal-Copula-Based Fusion of Spaceborne and Airborne SAR Images for Ship Target Detection⁎⁎

Wang, Xueqian; Zhu, Dong; Li, Gang; Zhang, Xiaoping; He, You

doi:10.1016/j.inffus.2021.07.019

Cited by 15 publications

(3 citation statements)

References 40 publications

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“…The first method consists in assigning labels to the local structure pixels based on the distance between them and the thumbnail cluster center. As shown in Equation (10), we can compare the distance between pixel p ij and each cluster center o j and assign the label of the class with the smallest distance to the pixel.…”

Section: Non-thumbnail Pixel Segmentationmentioning

confidence: 99%

“…Thus, obtaining accurate prior information is the prerequisite and foundation for supervised classification. Typical traditional supervised classification methods include the Bayesian, random forest, reinforcement learning, linear regression, support vector machine (SVM), decision tree, and neural network methods [7][8][9][10][11]. At present, deep convolutional neural network (CNN) is the most popular supervised classification method, which has been successfully applied in facial recognition and language processing.…”

Section: Introductionmentioning

confidence: 99%

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Ship Target Detection Method in Synthetic Aperture Radar Images Based on Block Thumbnail Particle Swarm Optimization Clustering

Huang,

Zhang,

Chen

2023

Remote Sensing

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Ship target detection is an important application of synthetic aperture radar (SAR) imaging remote sensing in ocean monitoring and management. However, SAR imaging is a form of coherence imaging, meaning that there is a large amount of speckle noise in each SAR image. This seriously affects the detection of an SAR image ship target when the fuzzy C-means (FCM) clustering method is used, resulting in numerous errors and incomplete detection. It is also associated with a slow detection speed, which easily falls into the local minima. To overcome these issues, a new method based on block thumbnail particle swarm optimization clustering (BTPSOC) was proposed for SAR image ship target detection. The BTPSOC algorithm uses block thumbnails to segment the main pixels, which improves the resistance to noise interference and segmentation accuracy, enhances the ability to process different types of SAR images, and reduces the runtime. When particle swarm optimization (PSO) technology is used to optimize the FCM clustering center, global optimization is achieved, the clustering performance is improved, the risk of falling into the local minima is overcome, and the stability is improved. The SAR images from two datasets containing ship targets were used in verification experiments. The experimental results show that the BTPSOC algorithm can effectively detect the ship target in SAR images and that it maintains good integrity with regard to the detailed information from the target region. At the same time, experiments comparing the deep convolution neural network (CNN) and constant false alarm rate (CFAR) were conducted.

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Section: Non-thumbnail Pixel Segmentationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Ship Target Detection Method in Synthetic Aperture Radar Images Based on Block Thumbnail Particle Swarm Optimization Clustering

Huang,

Zhang,

Chen

2023

Remote Sensing

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show abstract

“…Object detection in remote sensing images involves the automated identification and localization of specific targets of interest within the imagery. This technology has proven invaluable in diverse applications such as natural disaster detection [4] and ship detection [5] [6]. Initially, object detection in remote sensing heavily relied on traditional methods [7] such as template matching [8] [9], expert knowledge [10] [11], and object-based image analysis (OBIA) [12].…”

mentioning

confidence: 99%

FSOD4RSI: Few-Shot Object Detection for Remote Sensing Images via Features Aggregation and Scale Attention

Gao,

Wu,

Wang

et al. 2024

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

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Due to the continuous development of few-shot learning, there have been notable advancements in methods for few-shot object detection in recent years. However, most existing methods in this domain primarily focus on natural images, neglecting the challenges posed by variations in object scales, which are usually encountered in remote sensing images. This paper proposes a new few-shot object detection model designed to handle the issue of object scale variation in remote sensing images. Our developed model has two essential parts: a feature aggregation module (FAM) and a scale-aware attention module (SAM). Considering the few-shot features of remote sensing images, we designed the FAM to improve the support and query features through channel multiplication operations utilizing a feature pyramid network (FPN) and a Transformer encoder. The created FAM better extracts the global features of remote sensing images and enhances the significant feature representation of fewshot remote sensing objects. Additionally, we design the SAM to address the scale variation problems that frequently occur in remote sensing images. By employing multiscale convolutions, the SAM enables the acquisition of contextual features while adapting to objects of varying scales. Extensive experiments were conducted on benchmark datasets, including NWPU VHR-10 and DIOR datasets, and the results show that our model indeed addresses the challenges posed by object scale variation and improves the applicability of few-shot object detection in the remote sensing domain.

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Ship detection based on deep learning using SAR imagery: a systematic literature review

Yasir

Wan

et al. 2022

Soft Comput

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Proposal-Copula-Based Fusion of Spaceborne and Airborne SAR Images for Ship Target Detection⁎⁎

Cited by 15 publications

References 40 publications

Ship Target Detection Method in Synthetic Aperture Radar Images Based on Block Thumbnail Particle Swarm Optimization Clustering

Ship Target Detection Method in Synthetic Aperture Radar Images Based on Block Thumbnail Particle Swarm Optimization Clustering

FSOD4RSI: Few-Shot Object Detection for Remote Sensing Images via Features Aggregation and Scale Attention

Ship detection based on deep learning using SAR imagery: a systematic literature review

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