Abstract
Abstract. Real-time ship detection using synthetic aperture radar (SAR) plays a vital role in disaster emergency and marine security. Especially the high resolution and wide swath (HRWS) SAR images, provides the advantages of high resolution and wide swath synchronously, significantly promotes the wide area ocean surveillance performance. In this study, a novel method is developed for ship target detection by using the HRWS SAR images. Firstly, an adaptive sliding window is developed to propose the suspected ship target areas, based upon the analysis of SAR backscattering intensity images. Then, backscattering intensity and texture features extracted from the training samples of manually selected ship and non-ship slice images, are used to train a support vector machine (SVM) to classify the proposed ship slice images. The approach is verified by using the Sentinl1A data working in interferometric wide swath mode. The results demonstrate the improvement performance of the proposed method over the constant false alarm rate (CFAR) method, where the classification accuracy improved from 88.5 % to 96.4 % and the false alarm rate mitigated from 11.5 % to 3.6 % compared with CFAR respectively.
Highlights
Ship detection is of great significance for maritime surveillance, fishery activity management and illegally operating ships monitoring (Ouchi, 2013)
In terms of the problems mentioned above, this paper presents an adaptive algorithm for ship detection in the ocean by using the High Resolution Wide Swath (HRWS) Synthetic Aperture Radar (SAR) images
A flowchart summarizing the method proposed in this study is presented in Fig. 1, including the following steps: water body extraction, ship areas proposal, Gray-level co-occurrence Matrix (GLCM) texture feature extraction and support vector machine (SVM) classification
Summary
Ship detection is of great significance for maritime surveillance, fishery activity management and illegally operating ships monitoring (Ouchi, 2013). The features of metal material ships are more obvious for detection with ocean background. This mirrors out the specified the advantages of employing SAR systems in ship surveillance (Roarty, 2011). Two Parameter CFAR algorithm employs the Gaussian distribution to estimate the background clusters, and three sliding windows are employed to go through the images. KSW double threshold method uses the idea of information entropy into the image segmentation. The methodology can be described as to find the maximum value of the sum of the information entropies after image classification In this case, strong Bragg resonance will occur under the terrible ocean environment, especially when ocean surface is accompanied by strong wind. The ships can be detected under complicated ocean conditions
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