Journal
REMOTE SENSING
Volume 10, Issue 3, Pages -Publisher
MDPI
DOI: 10.3390/rs10030400
Keywords
remote sensing; visual saliency; radial gradient transform; covariance matrix; Gaussian SVM
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Funding
- National Defense Pre-Research Foundation of China [402040203]
- Programs Foundation of Key Laboratory of Airborne Optical Imaging and Measurement, Chinese Academy of Science [y3hc1sr141]
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Major challenges for automatic ship detection in optical remote sensing (ORS) images include cloud, wave, island, wake clutters, and even the high variability of targets. This paper presents a practical ship detection scheme to resolve these existing issues. The scheme contains two main coarse-to-fine stages: prescreening and discrimination. In the prescreening stage, we construct a novel visual saliency detection method according to the difference of statistical characteristics between highly non-uniform regions which allude to regions of interest (ROIs) and homogeneous backgrounds. It can serve as a guide for locating candidate regions. In this way, not only can the targets be precisely detected, but false alarms are also significantly reduced. In the discrimination stage, to get a better representation of the target, both shape and texture features characterizing the ship target are extracted and concatenated as a feature vector for subsequent classification. Moreover, the combined feature is invariant to the rotation. Finally, a trainable Gaussian support vector machine (SVM) classifier is performed to validate real ships out of ship candidates. We demonstrate the superior performance of the proposed hierarchical detection method with detailed comparisons to existing efforts.
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