Ship Target Detection Method in Synthetic Aperture Radar Images Based on Block Thumbnail Particle Swarm Optimization Clustering
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 dete...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/20/4972 |
| _version_ | 1827719960837750784 |
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| author | Shiqi Huang Ouya Zhang Qilong Chen |
| author_facet | Shiqi Huang Ouya Zhang Qilong Chen |
| author_sort | Shiqi Huang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T20:55:57Z |
| format | Article |
| id | doaj.art-b3a5c99e2535413eb5613395f0f9c0be |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T20:55:57Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-b3a5c99e2535413eb5613395f0f9c0be2023-11-19T17:59:02ZengMDPI AGRemote Sensing2072-42922023-10-011520497210.3390/rs15204972Ship Target Detection Method in Synthetic Aperture Radar Images Based on Block Thumbnail Particle Swarm Optimization ClusteringShiqi Huang0Ouya Zhang1Qilong Chen2School of Information Technology and Engineering, Guangzhou College of Commerce, Guangzhou 511363, ChinaSchool of Information Technology and Engineering, Guangzhou College of Commerce, Guangzhou 511363, ChinaSchool of Information Technology and Engineering, Guangzhou College of Commerce, Guangzhou 511363, ChinaShip 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.https://www.mdpi.com/2072-4292/15/20/4972SAR imageship target detectionblock thumbnailsparticle swarm optimizationdeep convolution neural network |
| spellingShingle | Shiqi Huang Ouya Zhang Qilong Chen Ship Target Detection Method in Synthetic Aperture Radar Images Based on Block Thumbnail Particle Swarm Optimization Clustering Remote Sensing SAR image ship target detection block thumbnails particle swarm optimization deep convolution neural network |
| title | Ship Target Detection Method in Synthetic Aperture Radar Images Based on Block Thumbnail Particle Swarm Optimization Clustering |
| title_full | Ship Target Detection Method in Synthetic Aperture Radar Images Based on Block Thumbnail Particle Swarm Optimization Clustering |
| title_fullStr | Ship Target Detection Method in Synthetic Aperture Radar Images Based on Block Thumbnail Particle Swarm Optimization Clustering |
| title_full_unstemmed | Ship Target Detection Method in Synthetic Aperture Radar Images Based on Block Thumbnail Particle Swarm Optimization Clustering |
| title_short | Ship Target Detection Method in Synthetic Aperture Radar Images Based on Block Thumbnail Particle Swarm Optimization Clustering |
| title_sort | ship target detection method in synthetic aperture radar images based on block thumbnail particle swarm optimization clustering |
| topic | SAR image ship target detection block thumbnails particle swarm optimization deep convolution neural network |
| url | https://www.mdpi.com/2072-4292/15/20/4972 |
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