Abstract
The surveillance of maritime areas with remote sensing is vital for security reasons, as well as for the protection of the environment. Satellite-borne synthetic aperture radar (SAR) offers large-scale surveillance, which is not reliant on solar illumination and is rather independent of weather conditions. The main feature of vessels in SAR images is a higher backscattering compared to the sea background. This peculiarity has led to the development of several ship detectors focused on identifying anomalies in the intensity of SAR images. More recently, different approaches relying on the information kept in the spectrum of a single-look complex (SLC) SAR image were proposed. This paper is focused on two main issues. Firstly, two recently developed sub-look detectors are applied for the first time to ship detection. Secondly, new and well-known ship detection algorithms are compared in order to understand which has the best performance under certain circumstances and if the sub-look analysis improves ship detection. The comparison is done on real SAR data exploiting diversity in frequency and polarization. Specifically, the employed data consist of six RADARSAT-2 fine quad-polacquisitions over the North Sea, five TerraSAR-X HH/VV dual-polarimetric data-takes, also over the North Sea, and one ALOS-PALSAR quad-polarimetric dataset over Tokyo Bay. Simultaneously to the SAR images, validation data were collected, which include the automatic identification system (AIS) position of ships and wind speeds. The results of the analysis show that the performance of the different sub-look algorithms considered here is strongly dependent on polarization, frequency and resolution. Interestingly, these sub-look detectors are able to outperform the classical SAR intensity detector when the sea state is particularly high, leading to a strong clutter contribution. It was also observed that there are situations where the performance improvement thanks to the sub-look analysis is not so noticeable.
Highlights
Monitoring maritime areas with remote sensing is a valuable topic, thanks to the possibility of observing areas that are too large to be properly characterized by sporadic in situ visits [1]
Ship detection using synthetic aperture radar (SAR) data is a relevant application of remote sensing, and as a result, several methodologies have been proposed in the last few decades
This paper focused on algorithms exploiting spectral analysis (i.e., Fourier transform of the complex SAR image)
Summary
Monitoring maritime areas with remote sensing is a valuable topic, thanks to the possibility of observing areas that are too large to be properly characterized by sporadic in situ visits [1]. This paper deals with the issue of vessel surveillance or ship detection, which is a crucial topic for several reasons, including security and protection of the environment. An example of the latter is the monitoring of no-navigation zones, such as natural parks. Illegal fishery could be detected by surveilling the protected areas with remote sensing In this context, synthetic aperture radar (SAR) [2] has the advantage of providing high-resolution images, which is extremely valuable for the detection of small vessels (a few tens of meters long, as can be the case for fishing boats) [3]. These are the generalized likelihood ratio test (GLRT) of sub-look images [4,5] and the sub-look entropy [6]
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