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A constant false alarm rate (CFAR) detecting method for ships in high-resolution dual-polarization synthetic aperture radar (SAR) amplitude images has been proposed in this paper. First, by the production of amplitude images from two polarimetric channels, a novel detector simply called the PMA detector has been constructed. We testified that the P...
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... Object detection technology [27] under the framework of deep learning has made practical progress in many fields, such as detection of coastline garbage [28], animal species [29], and vehicles [30], which greatly facilitates people's work. In the application of SAR remote sensing, object detection technology is mainly used to identify ships [31] and offshore oil spills [32]. By combining the object detection technology, the subsidence areas could be automatically identified in the wide differential interferogram, and the small-scale subsidence areas could be located. ...
Long-term industrial activities tend to cause surface subsidence and damage to ground facilities and local ecological environment. Monitoring and analyzing surface subsidence is of great significance to prevent potential disasters. The surface type of the Yellow River Delta in China is complex and there are many industrial activities, so it is necessary to monitor the surface subsidence in this area. Small Baseline Subset InSAR (SBAS-InSAR) can monitor the surface subsidence with millimeter-level accuracy, but it takes a long time to process wide images (Sentinel-1) and is seriously affected by atmospheric errors. To avoid these limitations, we constructed a method combining the CenterNet network and SBAS-InSAR (CNSBAS-InSAR). Firstly, the CenterNet network is used to automatically detect the subsidence areas from the wide differential interferogram formed by two SAR satellite images and determine the location of the subsidence area. Then, the SBAS-InSAR monitoring is performed on the detected multiple subsidence areas. Finally, the small-scale subsidence results are obtained. In this study, based on 24 Sentinel-1A satellite images acquired from 10 January 2018 to 24 December 2018, nine subsidence areas in Yellow River Delta were detected. Three of them had long-term surface subsidence. They were located in Zhanhua District, Xianhe Town, and Hongguang Village, respectively. This paper focuses on analyzing these three areas. The maximum subsidence rate of Zhanhua District, Xianhe Town, and Hongguang Village were −135.21 mm/a, −330.91 mm/a, and −209.68 mm/a, respectively. In addition, the analysis showed that precipitation in the Zhanhua District could effectively slow down the subsidence rate of the area. The subsidence of Xianhe Town threatened the safety of the Shugang Expressway. The subsidence of Hongguang Village caused the safety risks of buildings. The results of this study prove that CNSBAS-InSAR method is reliable for monitoring subsidence areas and it can provide a reference for local construction and protection of Yellow River Delta.
... Compared with the single-polarization SAR data, multi-polarization data contains richer information on backscattering and phase and provides a much more comprehensive characterization of the polarization information of complex sea conditions. Based on the dual-polarization SAR data, Gao et al. (Gao et al., 2013) proposed a ship CFAR detection method, which constructs a new PMA detector with improved ship SCR and makes it easier to detect ships from clutter. Shirvany et al. (Shirvany et al., 2012) based on the fact that the degree of polarization (DoP) can describe the fundamental quantity of partially polarized electromagnetic fields, studied the performance of DoP by combining ship and oil-spill detection under different polarization in hybrid, compact, and linear dual-pol SAR images. ...
Synthetic aperture radar (SAR) is considered being a good option for earth observation with its unique advantages. In this paper, we proposed an adaptive ship detector using full-polarization SAR images. First, by thoroughly investigating the scattering characteristics between ships and their background, and the wave polarization anisotropy, a novel ship detector is proposed by jointing the two characteristics, named Scattering-Anisotropy joint (joint-SA). Based on the theoretical analysis, we showed that the joint-SA is an effective physical quantity to show the difference between the ship and its background, and thus joint-SA can be used for ship detection of full-polarization image data. Second, the generalized Gamma distribution was used to characterize the joint-SA statistics of sea clutter with a large range of homogeneity. As a result, an adaptive constant false alarm rate (CFAR) method was implemented based on the joint-SA. Finally, RADARSAT-2 and GF-3 data in C-band and ALOS data in L-band are used for verification. We tested on five datasets, and the experimental results verify the correctness and superiority of the constant false alarm rate (CFAR) method based on the joint-SA. In addition, the experimental results also showed that the signal-clutter ratio (SCR) of the proposed ship detector joint-SA (33.17 dB, 35.98 dB, 57.25 dB) is better than that of DBSP (8.92 dB, 3.43 dB, 25.40 dB) and RsDVH (17.28 dB, 11.17 dB, 54.55 dB). More importantly, the proposed detector joint-SA has higher detection accuracy and a lower false alarm rate.
... This technique shows better performance when cross-polarisation signal becomes weak but may produce false alarms due to ship wakes. The approach, based on using geometric mean, Equation (2), is less affected by ship wakes and other signals not observed at co-and cross-polarisations [23,24]. ...
Over the last decade, West African coastal countries, including Ghana, have experienced extensive economic damage due to illegal, unreported and unregulated (IUU) fishing activity, estimated at about USD 100 million in losses each year. Illegal, unreported and unregulated fishing poses an enormous threat to the conservation and management of the dwindling fish stocks, causing multiple adverse consequences for fisheries, coastal and marine ecosystems and for the people who depend on these resources. The Integrated System for Surveillance of Illegal, Unlicensed and Unreported Fishing (INSURE) is an efficient and inexpensive system that has been developed for the monitoring of IUU fishing in Ghanaian waters. It makes use of fast-delivery Earth observation data from the synthetic aperture radar instrument on Sentinel-1 and the Multi Spectral Imager on Sentinel-2, detecting objects that differ markedly from their immediate background using a constant false alarm rate test. Detections are matched to, and verified by, Automatic Identification System (AIS) data, which provide the location and dimensions of ships that are legally operating in the region. Matched and unmatched data are then displayed on a web portal for use by coastal management authorities in Ghana. The system has a detection success rate of 91% for AIS-registered vessels, and a fast throughput, processing and delivering information within 2 h of acquiring the satellite overpass. However, over the 17-month analysis period, 75% of SAR detections have no equivalent in the AIS record, suggesting significant unregulated marine activity, including vessels potentially involved in IUU. The INSURE system demonstrated its efficiency in Ghana’s exclusive economic zone and it can be extended to the neighbouring states in the Gulf of Guinea, or other geographical regions that need to improve fisheries surveillance.
... The constant false alarm rate (CFAR), with a sliding window, has been widely used to detect ship targets in SAR images [10][11][12][13][14], but it has the disadvantage that the method is very time consuming. For SAR satellite images, which provide dual or quad polarization, many algorithms based on multi-polarimetric radar systems have been developed [15][16][17][18][19]. Moreover, machine learning techniques have been used recently to detect ship targets in SAR images [1,[20][21][22][23][24]. ...
In this paper, an automatic ship detection method using the artificial neural network (ANN) and support vector machine (SVM) from X-band SAR satellite images is proposed. When using machine learning techniques, the most important points to consider are (i) defining the proper input neurons and (ii) selecting the correct training data. We focused on generating two optimal input data neurons that (i) strengthened ship targets and (ii) mitigated noise effects by image processing techniques, including median filtering, multi-looking, etc. The median filter and multi-look operations were used to reduce the background noise, and the median filter operation was also used to remove ships in an image in order to maximize the difference between the pixel values of ships and the sea. Through the root-mean-square difference calculation, most ship targets, even including small ships, were emphasized in the images. We tested the performance of the proposed method using X-band high-resolution SAR images including COSMO-SkyMed, KOMPSAT-5, and TerraSAR-X images. An intensity difference map and a texture difference map were extracted from the X-band SAR single-look complex (SLC) images, and then, the maps were used as input neurons for the ANN and SVM machine learning techniques. Finally, we created ship-probability maps through the machine learning techniques. To validate the ANN and SVM results, optimal threshold values were obtained by using the statistical approach and then used to identify ships from the ship-probability maps. Consequently, the level of recall achieved was greater than 90% in most cases. This means that the proposed method enables the detection of most ship targets from X-band SAR images with a reduced number of false detections from negative effects.
... Since SAR is a promising technique for identifying maritime ships at night, where the visual system might fail to work [72,73,74]. With this point, in contrast with many SAR related works [72,73,74], this section provides an implementation study with respect to maritime objects identification using passive SAR system using GNSS signals. ...
... Since SAR is a promising technique for identifying maritime ships at night, where the visual system might fail to work [72,73,74]. With this point, in contrast with many SAR related works [72,73,74], this section provides an implementation study with respect to maritime objects identification using passive SAR system using GNSS signals. The comprehensively combined proposed algorithms from chapter 3 to chapter 5 continues to be used for SAR imaging, where the combination is processed in the same manner as aforementioned in section 6.2. ...
Passive Global Navigation Satellite System (GNSS)-based Synthetic Aperture Radar (SAR), known as GNSS-SAR, is a currently developing passive radar sensing system. As the system works in passive mode, GNSS-SAR is much cheaper with a much smaller size, thus it is more flexible to be installed under many application scenarios. However because of the restriction of GNSS signal bands, the resolution of GNSS-SAR is lower than conventional SAR. Also, the weak reflected GNSS signals is another limitation for the application of GNSS-SAR. Due to the fact that GNSS signals are low Equivalent Isotropically Radiated Power (EIRP) sources, the signal strength after reflection will be very weak.
In this study, a new GNSS-SAR imaging algorithm is proposed to improve object detectability under weak reflected signals. Both theoretical analysis and experimental study show that the proposed algorithm can result in obviously enhanced imaging detectability. For instance, using GPS C/A code signal receiver, the proposed algorithm can detect the object with the signal strength as low as -160 dBm, while the conventional algorithm cannot. Meanwhile, computation with the proposed imaging algorithm is significantly more efficient than with conventional GNSS-SAR imaging algorithm.
To enhance range resolution, two new range compression algorithms are proposed to reduce the compressed ambiguity of main-lobe due to chip rate of the respective pseudo-random noise (PRN) code, respectively. In first proposed algorithm (see Chapter 4), range compression is carried out by correlating a reflected GNSS intermediate frequency (IF) signal with a synchronized direct GNSS base-band signal at range domain, where the main lobe ambiguity of the compressed pulse is narrowed down. Thereafter, spectrum equalization technique is applied to the compressed results for suppressing side lobes to obtain a final range-compressed signal. In the second proposed algorithm (see Chapter 5), the main-lobe ambiguity of range compressed signal is deduced by applying Diff2 peak extraction method. Both simulation and field experimental results demonstrate that the proposed range compression algorithms contribute to the resolution enhancement very significantly. For example, on the basis of GPS C/A code receiver platform with the IF value 4 MHz and sampling rate 16 MHz, the first proposed algorithm can improve the best attainable range resolution to 40 m level, while the second proposed algorithm can enhance the best attainable range resolution to 36 m level, compared to the best attainable range resolution 171 m provided by conventional range compression algorithm. In contrast with many current GNSS-SAR research works, the major novelty of the proposed range compression algorithms is that range compressed pulse ambiguity caused by PRN code correlation function has been addressed successfully.
... In the second class of ship detection approaches, a variety of scattering power detectors, e.g., the polarimetric SPAN (total power) detector, polarimetric whitening filter (PWF), and power maximization synthesis (PMS) detector [7], [8], are developed to fuse multichannel polarimetric information to discriminate ships from clutter. Although these detectors can detect ships, they may still lose the weak ship whose backscattered intensity is weak [9]. In the literature, there are also many other ways of using the scattering mechanisms to detect ships, e.g., the polarimetric entropy (H) [10], the symmetry scattering characterization method [11], and the polarimetric scattering similarity [12]. ...
... The reason behind the fact may lie in the calculation of PCDM. From (9), it can be seen that the calculation of PCDM matrix involves an accumulation of the local spatial difference of the polarimetric covariance features, which make the PCDM matrix to be of more discriminative than the original polarimetric covariance matrix in distinguishing targets from clutter. Furthermore, in our approach, the PCDM matrix is decomposed to calculate a new polarimetric signature, called pedestal ship height (PSH), which is shown to be able to distinguish real ships from their azimuth ambiguities and clutter. ...
Ship detection using Polarimetric SAR data has attracted a lot of attention in recent years. Due to the sampling of the Doppler spectrum at finite intervals of the pulse repetition frequency, the azimuth ambiguities often appear in PolSAR images, which make the ship detection in PolSAR images frequently generating false alarms, especially in the case of low backscattering sea environment. In order to handle the problem and improve the performance of ship detection in PolSAR images, this paper presents a new method, which is mainly based on concentrating the polarimetric difference between ship pixels and background pixels. We first calculate a polarimetric covariance difference matrix, denoted as polarimetric covariance difference matrix (PCDM), by accumulating the elemental difference between the polarimetric covariance matrix at each pixel and the counterparts in its 3 × 3 neighbors. The SPAN detector is then applied on PCDM to obtain a coarse detection result. Meanwhile, we decompose the PCDM matrix to calculate a new polarimetric signature, called pedestal ship height (PSH), and use it together with the coarse detection result to distinguish ships from ambiguities. Extensive experiments on three real PolSAR datasets are carried out to demonstrate the effectiveness of the proposed method in comparing with other algorithms. The experimental results show that the proposed method not only detects ships effectively, but also can remove the azimuth ambiguities and reduce the false alarms significantly.
... The motivation for this approach is that individual scatterers on the ship can be different ones in HH, VV or cross-pol, and in this way, all are equally combined to better fill in the outline of the ship. In the literature, other approaches to incoherently combine multiple polarization channels have been proposed: Cross-correlation [62], multiplication [63,64], principle component analysis [65] or summation after normalization [66]. ...
Search for Unidentified Maritime Objects (SUMO) is an algorithm for ship detection in satellite Synthetic Aperture Radar (SAR) images. It has been developed over the course of more than 15 years, using a large amount of SAR images from almost all available SAR satellites operating in L-, C- and X-band. As validated by benchmark tests, it performs very well on a wide range of SAR image modes (from Spotlight to ScanSAR) and resolutions (from 1-100 m) and for all types and sizes of ships, within the physical limits imposed by the radar imaging. This paper describes, in detail, the algorithmic approach in all of the steps of the ship detection: land masking, clutter estimation, detection thresholding, target clustering, ship attribute estimation and false alarm suppression. SUMO is a pixel-based CFAR (Constant False Alarm Rate) detector for multi-look radar images. It assumes a K distribution for the sea clutter, corrected however for deviations of the actual sea clutter from this distribution, implementing a fast and robust method for the clutter background estimation. The clustering of detected pixels into targets (ships) uses several thresholds to deal with the typically irregular distribution of the radar backscatter over a ship. In a multi-polarization image, the different channels are fused. Azimuth ambiguities, a common source of false alarms in ship detection, are removed. A reliability indicator is computed for each target. In post-processing, using the results of a series of images, additional false alarms from recurrent (fixed) targets including range ambiguities are also removed. SUMO can run in semi-automatic mode, where an operator can verify each detected target. It can also run in fully automatic mode, where batches of over 10,000 images have successfully been processed in less than two hours. The number of satellite SAR systems keeps increasing, as does their application to maritime surveillance. The open data policy of the EU's Copernicus program, which includes the Sentinel-1 satellite, has hugely increased the availability of SAR images. This paper aims to cater to the consequently expected wider demand for knowledge about SAR ship detectors.
... Herein, the two polarimetric channels refer to the commonly used co-and cross-polarized channels acquired simultaneously and also refer to two copolarized channels in a peculiarity of the COSMO-SkyMed PingPong mode due to a time delay [30]. For the former, a metric called the product of multilook amplitudes (PMA) detector was recently defined [32] to indicate the correlation between co-and cross-polarized channels. A consistent expression of the PMA detector is found in [29] and [31]. ...
... In [32], a PMA detector/metric is defined as ...
The product of multilook amplitudes (PMA) detector has been used to detect ships in high-resolution dual-polarization synthetic-aperture-radar images. However, the adaptive constant false-alarm rate (CFAR) technique of the PMA detector is desirable for practical applications, wherein a crucial problem is to find an appropriate model to describe the PMA statistics for varied sea surfaces. First, we consider a new probability density function to characterize the PMA statistics of homogeneous sea surfaces. Second, by using the new density and multiplicative model, the PMA detector's statistical model for nonhomogeneous sea surfaces is specified and demonstrated to be the G(0) distribution. Then, a theoretical analysis of the relationship between the performance of the standard CFAR detection and the parameters in the G(0) distribution is conducted. Experiments performed on the measured RADARSAT-2 and NASA/JPL AIRSAR images verify the effectiveness and appropriateness of the G(0) model for describing the statistical behavior of the PMA of sea clutter, as well as the usefulness of the model for practical ship-detection applications.
... In January 2013 [8], author introduced at the high resolution and dual polarization of SAR amplitude, a CFAR detecting method aims at adaptive detection of ship to differentiate the ship from clutter easily and to improve the signal to clutter ratio, a Novel PMA detector is being designed. Different type of experiments is being performed on measured dual polarization. ...
... In September 2012 [18], author taken Reprojected MODIS image and applied Temposeg method to calculate the ice floe area as a function of time. In January 2013 [8], author worked on detection of moving ship in sea. Author has designed a PMA detector to detect ship more accurately and to improve signal to clutter ratio. ...
A synthetic aperture radar is crucial for detecting ships. In this study, to improve the detection capability of weakly scattering targets and reduce false alarms, a detector
$F_{vh}$
based on the scattering characteristics of ships is proposed after an in-depth study of the differences between ship and sea surface scattering mechanisms. The volume scattering mechanism and the helix scattering mechanism are fused, and the volume scattering power is used to describe the scattering structure on the ship, and the helix scattering power is used to increase the difference between the ship and the sea surface. We test on 4 GF-3 fully polarized synthetic aperture radar data. The experimental results show that
$F_{vh}$
has improved detection capability and can successfully minimize false alarm generation.
