Confidence-Aware Ship Classification Using Contour Features in SAR Images
In this paper, a novel set of 13 handcrafted features derived from the contours of ships in synthetic aperture radar (SAR) images is introduced for ship classification. Additionally, the information entropy is presented as a valuable metric for quantifying the confidence (or uncertainty) associated...
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| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/1/127 |
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| author | Al Adil Al Hinai Raffaella Guida |
| author_facet | Al Adil Al Hinai Raffaella Guida |
| author_sort | Al Adil Al Hinai |
| collection | DOAJ |
| description | In this paper, a novel set of 13 handcrafted features derived from the contours of ships in synthetic aperture radar (SAR) images is introduced for ship classification. Additionally, the information entropy is presented as a valuable metric for quantifying the confidence (or uncertainty) associated with classification predictions. Two segmentation methods for the contour extraction were investigated: a classical approach using the watershed algorithm and a U-Net architecture. The features were tested using a support vector machine (SVM) on the OpenSARShip and FUSAR-Ship datasets, demonstrating improved results compared to existing handcrafted features in the literature. Alongside the SVM, a random forest (RF) and a Gaussian process classifier (GPC) were used to examine the effect of entropy derivation from different classifiers while assessing feature robustness. The results show that when aggregating predictions of an ensemble, techniques such as entropy-weighted averaging are shown to produce higher accuracies than methods like majority voting. It is also found that the aggregation of individual entropies within an ensemble leads to a normal distribution, effectively minimizing outliers. This characteristic was utilized to model the entropy distributions, from which confidence levels were established based on Gaussian parameters. Predictions were then assigned to one of three confidence levels (high, moderate, or low), with the Gaussian-based approach showing superior correlation with classification accuracy compared to other methods. |
| format | Article |
| id | doaj-art-a32e561d9b9c4f74b6ef8dd45a0b5e11 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-a32e561d9b9c4f74b6ef8dd45a0b5e112025-01-10T13:20:18ZengMDPI AGRemote Sensing2072-42922025-01-0117112710.3390/rs17010127Confidence-Aware Ship Classification Using Contour Features in SAR ImagesAl Adil Al Hinai0Raffaella Guida1Surrey Space Centre, University of Surrey, Guildford GU2 7XH, UKSurrey Space Centre, University of Surrey, Guildford GU2 7XH, UKIn this paper, a novel set of 13 handcrafted features derived from the contours of ships in synthetic aperture radar (SAR) images is introduced for ship classification. Additionally, the information entropy is presented as a valuable metric for quantifying the confidence (or uncertainty) associated with classification predictions. Two segmentation methods for the contour extraction were investigated: a classical approach using the watershed algorithm and a U-Net architecture. The features were tested using a support vector machine (SVM) on the OpenSARShip and FUSAR-Ship datasets, demonstrating improved results compared to existing handcrafted features in the literature. Alongside the SVM, a random forest (RF) and a Gaussian process classifier (GPC) were used to examine the effect of entropy derivation from different classifiers while assessing feature robustness. The results show that when aggregating predictions of an ensemble, techniques such as entropy-weighted averaging are shown to produce higher accuracies than methods like majority voting. It is also found that the aggregation of individual entropies within an ensemble leads to a normal distribution, effectively minimizing outliers. This characteristic was utilized to model the entropy distributions, from which confidence levels were established based on Gaussian parameters. Predictions were then assigned to one of three confidence levels (high, moderate, or low), with the Gaussian-based approach showing superior correlation with classification accuracy compared to other methods.https://www.mdpi.com/2072-4292/17/1/127synthetic aperture radar (SAR)ship classificationmachine learning |
| spellingShingle | Al Adil Al Hinai Raffaella Guida Confidence-Aware Ship Classification Using Contour Features in SAR Images Remote Sensing synthetic aperture radar (SAR) ship classification machine learning |
| title | Confidence-Aware Ship Classification Using Contour Features in SAR Images |
| title_full | Confidence-Aware Ship Classification Using Contour Features in SAR Images |
| title_fullStr | Confidence-Aware Ship Classification Using Contour Features in SAR Images |
| title_full_unstemmed | Confidence-Aware Ship Classification Using Contour Features in SAR Images |
| title_short | Confidence-Aware Ship Classification Using Contour Features in SAR Images |
| title_sort | confidence aware ship classification using contour features in sar images |
| topic | synthetic aperture radar (SAR) ship classification machine learning |
| url | https://www.mdpi.com/2072-4292/17/1/127 |
| work_keys_str_mv | AT aladilalhinai confidenceawareshipclassificationusingcontourfeaturesinsarimages AT raffaellaguida confidenceawareshipclassificationusingcontourfeaturesinsarimages |