Ship detection and classification based on cascaded detection of hull and wake from optical satellite remote sensing imagery
Satellite remote-sensing provides a cost- and time-effective tool for ship monitoring at sea. Most existing approaches focused on extraction of ship locations using either hull or wake. In this paper, a method of cascaded detection of ship hull and wake was proposed to locate and classify ships usin...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2023-12-01
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| Series: | GIScience & Remote Sensing |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/15481603.2023.2196159 |
| _version_ | 1797679020551176192 |
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| author | Yingfei Liu Ruihao Zhang Ruru Deng Jun Zhao |
| author_facet | Yingfei Liu Ruihao Zhang Ruru Deng Jun Zhao |
| author_sort | Yingfei Liu |
| collection | DOAJ |
| description | Satellite remote-sensing provides a cost- and time-effective tool for ship monitoring at sea. Most existing approaches focused on extraction of ship locations using either hull or wake. In this paper, a method of cascaded detection of ship hull and wake was proposed to locate and classify ships using high-resolution satellite imagery. Candidate hulls were fast located through phase spectrum of Fourier transform. A hull refining module was then executed to acquire accurate shapes of candidate hull. False alarms were removed through the shape features and textures of candidate hulls. The probability that a candidate hull is determined as a real one increased with the presence of wakes. After true ships were determined, ship classification was conducted using a fuzzy classifier combining both hull and wake information. The proposed method was implemented to Gaofen-1 panchromatic and multispectral (PMS) imagery and showed good performance for ship detection with recall, precision, overall accuracy, and specificity of 90.1%, 88.1%, 98.8%, and 99.3%, respectively, better than other state-of-the-art coarse-to-fine ship detection methods. Ship classification was successfully achieved for ships with detected wakes. The accuracy of correct classification was 83.8% while the proportion of false classification was 1.0%. Factors influencing the accuracy of the developed method, including texture features and classifiers combination and key parameters of the method, were also discussed. |
| first_indexed | 2024-03-11T23:08:20Z |
| format | Article |
| id | doaj.art-565ac8415ffc429a82a773a968c1e76e |
| institution | Directory Open Access Journal |
| issn | 1548-1603 1943-7226 |
| language | English |
| last_indexed | 2024-03-11T23:08:20Z |
| publishDate | 2023-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | GIScience & Remote Sensing |
| spelling | doaj.art-565ac8415ffc429a82a773a968c1e76e2023-09-21T12:43:09ZengTaylor & Francis GroupGIScience & Remote Sensing1548-16031943-72262023-12-0160110.1080/15481603.2023.21961592196159Ship detection and classification based on cascaded detection of hull and wake from optical satellite remote sensing imageryYingfei Liu0Ruihao Zhang1Ruru Deng2Jun Zhao3Sun Yat-sen UniversityHuizhou UniversitySouthern Marine Science and Engineering Guangdong Laboratory (Zhuhai)Sun Yat-sen UniversitySatellite remote-sensing provides a cost- and time-effective tool for ship monitoring at sea. Most existing approaches focused on extraction of ship locations using either hull or wake. In this paper, a method of cascaded detection of ship hull and wake was proposed to locate and classify ships using high-resolution satellite imagery. Candidate hulls were fast located through phase spectrum of Fourier transform. A hull refining module was then executed to acquire accurate shapes of candidate hull. False alarms were removed through the shape features and textures of candidate hulls. The probability that a candidate hull is determined as a real one increased with the presence of wakes. After true ships were determined, ship classification was conducted using a fuzzy classifier combining both hull and wake information. The proposed method was implemented to Gaofen-1 panchromatic and multispectral (PMS) imagery and showed good performance for ship detection with recall, precision, overall accuracy, and specificity of 90.1%, 88.1%, 98.8%, and 99.3%, respectively, better than other state-of-the-art coarse-to-fine ship detection methods. Ship classification was successfully achieved for ships with detected wakes. The accuracy of correct classification was 83.8% while the proportion of false classification was 1.0%. Factors influencing the accuracy of the developed method, including texture features and classifiers combination and key parameters of the method, were also discussed.http://dx.doi.org/10.1080/15481603.2023.2196159ship detectionship classificationwake detectionoptical remote sensingmachine learning |
| spellingShingle | Yingfei Liu Ruihao Zhang Ruru Deng Jun Zhao Ship detection and classification based on cascaded detection of hull and wake from optical satellite remote sensing imagery GIScience & Remote Sensing ship detection ship classification wake detection optical remote sensing machine learning |
| title | Ship detection and classification based on cascaded detection of hull and wake from optical satellite remote sensing imagery |
| title_full | Ship detection and classification based on cascaded detection of hull and wake from optical satellite remote sensing imagery |
| title_fullStr | Ship detection and classification based on cascaded detection of hull and wake from optical satellite remote sensing imagery |
| title_full_unstemmed | Ship detection and classification based on cascaded detection of hull and wake from optical satellite remote sensing imagery |
| title_short | Ship detection and classification based on cascaded detection of hull and wake from optical satellite remote sensing imagery |
| title_sort | ship detection and classification based on cascaded detection of hull and wake from optical satellite remote sensing imagery |
| topic | ship detection ship classification wake detection optical remote sensing machine learning |
| url | http://dx.doi.org/10.1080/15481603.2023.2196159 |
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