Abstract
The convolution between co-polarization amplitude only data is studied to improve ship detection performance. The different statistical behaviors of ships and surrounding ocean are characterized a by two-dimensional convolution function (2D-CF) between different polarization channels. The convolution value of the ocean decreases relative to initial data, while that of ships increases. Therefore the contrast of ships to ocean is increased. The opposite variation trend of ocean and ships can distinguish the high intensity ocean clutter from ships' signatures. The new criterion can generally avoid mistaken detection by a constant false alarm rate detector. Our new ship detector is compared with other polarimetric approaches, and the results confirm the robustness of the proposed method.
Highlights
Ship detection by Synthetic Aperture Radar (SAR) has been widely used for monitoring fishing vessels, oil pollution, and traffic and immigration controls
This paper has studied ship detection with convolution between two co-polarization SAR data by applying the different statistical behavior of ships and ocean
The opposite variation trend of ocean and ships can be considered as a new criterion
Summary
Ship detection by Synthetic Aperture Radar (SAR) has been widely used for monitoring fishing vessels, oil pollution, and traffic and immigration controls. They suggested taking advantage of the fact that two different looks processed from different sub-apertures of a SAR system would be separated by a small time delay Owing to this time delay and different scattering mechanisms, there will be little correlation of the ocean backscatter in the two images but a large correlation in the backscatter from ships. Iehara et al [20] computed the two-dimensional cross correlation function of the two images for ship detection, while Ouchi and Yaguchi [21] calculated the correlation with coherence Both methods can detect ships, even when the intensity of the ship backscatter is similar to that of the surrounding ocean.
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