Ship Detection with Spectral Analysis of Synthetic Aperture Radar: A Comparison of New and Well-Known Algorithms

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 Remote Sens. 2015, 7 5417 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.

An Improved Shape Contexts Based Ship Classification in SAR Images

“…[Len, R, C, X, V, LRCS] T f = (8) In our study, for the sake of comparison, we conducted classification experiments with the improved shape contexts (ISC) method we propose, the original shape contexts (OSC) method, the feature proposed by the literature [15] with Euclidean distance and the conventional moment features, namely Hu moments and Zernike moments with Euclidean distance. In the ISC and OSC, we use five bins for log r and 12 equally spaced radial bins for θ .…”

“…To obtain more effective observation techniques, great attention to polarimetric approaches has been paid [5,6]. More recently, many researchers [7,8] propose different approaches relying on the information kept in the spectrum of a single-look complex (SLC) SAR images.…”

Sentinel-1/2 Data for Ship Traffic Monitoring on the Danube River

“…The automatic detection of ships based on SAR data benefits from a large number of scientific studies. An in-depth analysis of the new and well-known algorithms for SAR ship detection is performed in (Marino et al, 2015). The automatic classification of vessels using inverse synthetic aperture radar (ISAR) imagery is described in (Zhao et al, 2014b).…”

Wake Component Detection in X-Band SAR Images for Ship Heading and Velocity Estimation

“…Several international projects are investigating an operational ship monitoring system [1][2][3][4], and many experimental results are available on ship detection in SAR images [5][6][7][8][9][10]. In this ambit, ship wake detection not only has strong interest as the validation of ship presence, but also generates data useful for ship route estimation.…”