A Visual Search Inspired Computational Model for Ship Detection in Optical Satellite Images

Bi, Fukun; Zhu, Bocheng; Gao, Lining; Bian, Mingming

doi:10.1109/lgrs.2011.2180695

Cited by 110 publications

(7 citation statements)

References 11 publications

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“…Bi et al [14] employed a visual attention algorithm to highlight the positions of ships and provided their approximate regions.…”

Section: Visual-saliency-based Methodsmentioning

confidence: 99%

Ship Detection with Deep Learning in Optical Remote-Sensing Images: A Survey of Challenges and Advances

Zhao,

Wang,

et al. 2024

Remote Sensing

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Ship detection aims to automatically identify whether there are ships in the images, precisely classifies and localizes them. Regardless of whether utilizing early manually designed methods or deep learning technology, ship detection is dedicated to exploring the inherent characteristics of ships to enhance recall. Nowadays, high-precision ship detection plays a crucial role in civilian and military applications. In order to provide a comprehensive review of ship detection in optical remote-sensing images (SDORSIs), this paper summarizes the challenges as a guide. These challenges include complex marine environments, insufficient discriminative features, large scale variations, dense and rotated distributions, large aspect ratios, and imbalances between positive and negative samples. We meticulously review the improvement methods and conduct a detailed analysis of the strengths and weaknesses of these methods. We compile ship information from common optical remote sensing image datasets and compare algorithm performance. Simultaneously, we compare and analyze the feature extraction capabilities of backbones based on CNNs and Transformer, seeking new directions for the development in SDORSIs. Promising prospects are provided to facilitate further research in the future.

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“…Bi et al [14] employed a visual attention algorithm to highlight the positions of ships and provided their approximate regions.…”

Section: Visual-saliency-based Methodsmentioning

confidence: 99%

Ship Detection with Deep Learning in Optical Remote-Sensing Images: A Survey of Challenges and Advances

Zhao,

Wang,

et al. 2024

Remote Sensing

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“…For example, the benefits of automatic locating and tracking ships for the civil sector are maritime management, vessel traffic services, safety and rescue, fishery management, and illegal fishery surveillance. Moreover, the main applications in the military sector include naval warfare, battlefield environment assessment, and pirate activity surveillance [313][314][315]. RS has a leading role in SD and monitoring because of its several advantages, such as the availability of open-access multitemporal datasets and large area coverage.…”

Section: Ship Detection (Sd)mentioning

confidence: 99%

Ocean Remote Sensing Techniques and Applications: A Review (Part I)

Amani¹,

Moghimi

Mirmazloumi

et al. 2022

Water

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Oceans cover over 70% of the Earth’s surface and provide numerous services to humans and the environment. Therefore, it is crucial to monitor these valuable assets using advanced technologies. In this regard, Remote Sensing (RS) provides a great opportunity to study different oceanographic parameters using archived consistent multitemporal datasets in a cost-efficient approach. So far, various types of RS techniques have been developed and utilized for different oceanographic applications. In this study, 15 applications of RS in the ocean using different RS techniques and systems are comprehensively reviewed and discussed. This study is divided into two parts to supply more detailed information about each application. The first part briefly discusses 12 different RS systems that are often employed for ocean studies. Then, six applications of these systems in the ocean, including Ocean Surface Wind (OSW), Ocean Surface Current (OSC), Ocean Wave Height (OWH), Sea Level (SL), Ocean Tide (OT), and Ship Detection (SD), are provided. For each application, the applicable RS systems, their advantages and disadvantages, various RS and Machine Learning (ML) techniques, and several case studies are discussed. The other nine applications, including Iceberg, Sea Ice (SI), Sea Surface temperature (SST), Ocean Surface Salinity (OSS), Ocean Color (OC), Ocean Chlorophyll (OCh), Ocean Oil Spill (OOS), Underwater Ocean, and Fishery, are provided in Part II of this study.

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“…Automatic ship detection in optical satellite images has attracted intensive investigations (Bi et al, 2012 ; Jubelin and Khenchaf, 2014 ; Qi et al, 2015 ; Zou and Shi, 2016 ; Li et al, 2020 ). It plays a crucial role in a maritime surveillance system.…”

Section: Applications To Ship Detection In Optical Satellite Imagesmentioning

confidence: 99%

Visual Saliency via Multiscale Analysis in Frequency Domain and Its Applications to Ship Detection in Optical Satellite Images

Qian

2022

Front. Neurorobot.

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This article proposes a bottom-up visual saliency model that uses the wavelet transform to conduct multiscale analysis and computation in the frequency domain. First, we compute the multiscale magnitude spectra by performing a wavelet transform to decompose the magnitude spectrum of the discrete cosine coefficients of an input image. Next, we obtain multiple saliency maps of different spatial scales through an inverse transformation from the frequency domain to the spatial domain, which utilizes the discrete cosine magnitude spectra after multiscale wavelet decomposition. Then, we employ an evaluation function to automatically select the two best multiscale saliency maps. A final saliency map is generated via an adaptive integration of the two selected multiscale saliency maps. The proposed model is fast, efficient, and can simultaneously detect salient regions or objects of different sizes. It outperforms state-of-the-art bottom-up saliency approaches in the experiments of psychophysical consistency, eye fixation prediction, and saliency detection for natural images. In addition, the proposed model is applied to automatic ship detection in optical satellite images. Ship detection tests on satellite data of visual optical spectrum not only demonstrate our saliency model's effectiveness in detecting small and large salient targets but also verify its robustness against various sea background disturbances.

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A Visual Search Inspired Computational Model for Ship Detection in Optical Satellite Images

Cited by 110 publications

References 11 publications

Ship Detection with Deep Learning in Optical Remote-Sensing Images: A Survey of Challenges and Advances

Ship Detection with Deep Learning in Optical Remote-Sensing Images: A Survey of Challenges and Advances

Ocean Remote Sensing Techniques and Applications: A Review (Part I)

Visual Saliency via Multiscale Analysis in Frequency Domain and Its Applications to Ship Detection in Optical Satellite Images

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