A &amp;#x201C;dynamic&amp;#x201D; land masking algorithm for synthetic aperture radar images

Biamino, W.; Borasi, M.; Cavagnero, M.; Croce, Alessandro; Matteo, L. Di; Fontebasso, F.; Tataranni, F.; Trivero, P.

doi:10.1109/igarss.2015.7326783

Cited by 8 publications

(5 citation statements)

References 9 publications

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“…In [ 7 ], an algorithm for land masking is designed for ship detection in SAR images based on a series of image processing steps using mathematical transformation. The accuracy of the sea-land segmentation can be improved by evaluating the radiometric intensity gradient in zone closed to the sea in SAR image [ 8 ]. Experimental results from real SAR images demonstrate the usefulness of methods of wavelet transform [ 9 , 10 ] and threshold techniques [ 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

Ship Detection in Gaofen-3 SAR Images Based on Sea Clutter Distribution Analysis and Deep Convolutional Neural Network

Pan

You

2018

Sensors

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Target detection is one of the important applications in the field of remote sensing. The Gaofen-3 (GF-3) Synthetic Aperture Radar (SAR) satellite launched by China is a powerful tool for maritime monitoring. This work aims at detecting ships in GF-3 SAR images using a new land masking strategy, the appropriate model for sea clutter and a neural network as the discrimination scheme. Firstly, the fully convolutional network (FCN) is applied to separate the sea from the land. Then, by analyzing the sea clutter distribution in GF-3 SAR images, we choose the probability distribution model of Constant False Alarm Rate (CFAR) detector from K-distribution, Gamma distribution and Rayleigh distribution based on a tradeoff between the sea clutter modeling accuracy and the computational complexity. Furthermore, in order to better implement CFAR detection, we also use truncated statistic (TS) as a preprocessing scheme and iterative censoring scheme (ICS) for boosting the performance of detector. Finally, we employ a neural network to re-examine the results as the discrimination stage. Experiment results on three GF-3 SAR images verify the effectiveness and efficiency of this approach.

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Section: Introductionmentioning

confidence: 99%

Ship Detection in Gaofen-3 SAR Images Based on Sea Clutter Distribution Analysis and Deep Convolutional Neural Network

Pan

You

2018

Sensors

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“…During land masking, small land objects should be properly masked out in order to reduce the numbers of false alarms during ship detection (Brusch et al, 2011). Moreover, accurate land maps are also useful in assisting detection of sea surface slicks, wind and wave fields, coastal erosion, development of new structures along the coastline, and other features from high-resolution SAR images (Biamino et al, 2015; Jiang et al, 1999; Kim et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

“…One of the most commonly utilised shoreline databases is called the Global Self-consistent, Hierarchical, High-resolution Shoreline (GSHHS) which possess a good resolution shoreline data set (Brusch et al, 2011; Wessel and Smith, 1996). Another public domain land-water mask is the Shuttle Radar Topography Mission (SRTM) Water Body Data (SWBD) produced by exploiting data acquired during the National Aeronautics and Space Administration's (NASA's) space shuttle radar topography mission (Biamino et al, 2015; Karvonen, 2015; Wessel and Smith, 1996). However, compared to these coastline maps, Electronic Navigational Chart (ENC)-based coastline data is of much higher resolution.…”

Section: Introductionmentioning

confidence: 99%

“…For instance, despite the high quality of freely available coastline databases, they cannot be directly applied to satellite images as the images have often been georeferenced using only the coordinates of the four image corners without ground control points (Biamino et al, 2015; Greidanus et al, 2004; Trivero et al, 2016). Therefore, the geo-positioning of the satellite images has limited accuracy and results in image shifts in the azimuth direction, and a consequent displacement of the land mask resulting in shifting of shorelines from the actual coastline and associated false alarms near the coast (Biamino et al, 2015; Greidanus et al, 2004; Trivero et al, 2016). On the other hand, commonly available land masks usually have accuracy limitations with small islands, intertidal areas and coastal man-made constructions leading to false alarms (Greidanus et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

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An Improved Method of Land Masking for Synthetic Aperture Radar-based Ship Detection

2018

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Land masking of Synthetic Aperture Radar (SAR) images is generally accomplished by applying either archived shoreline databases or image segmentation. However, those methods cannot be solely applied to geographical areas complicated with many small islands and exposed rocks. Therefore, we have proposed a new procedure where Sobel edge extraction is applied to detect the edges of all objects from KOMPSAT-5 X-band SAR images, followed by a merging process with the edges from the land objects based on Electronic Navigational Chart (ENC) coastlines. Using the land mask data, geometrically corrected SAR images were masked before applying a ship detection algorithm. This land masking procedure was applied to several images covering different areas of the Korean Peninsula. The results show that land targets such as newly constructed and natural objects were also masked, and thus did not create false alarms during ship detection. Therefore, this method can be used to assist precise ship detection using SAR images in coastal waters.

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“…sandy beaches); however, this does not affect the average backscattering calculations, allowing OS detection near the coast. We tested this method on ENVISAT, ERS, RADARSAT, ALOS and COSMO/SkyMed images (see Figure 1) but it is ready to work with other coastlines' databases [5].…”

Section: Land Maskingmentioning

confidence: 99%

SEASAFE: an operational tool for sea surface monitoring with SAR

Biamino

Ananasso

Cavagnero

et al. 2015

Ravage of the Planet IV

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Synthetic Aperture Radar (SAR) is considered as a useful tool for sea surface monitoring, thanks to its capability to acquire images day and night and in all weather conditions. In particular, SAR is extensively used to detect oil slicks on the sea surface. Since the early 90s SAR acquisitions were routinely available with C-band sensors (such as ERS-1 and 2, Envisat, Radarsat 1 and 2). More recently, also L-band (ALOS PALSAR 1 and 2) and X-band (Terrasar X, COSMO/SkyMed constellation) sensors became operational. In this framework we developed a new software called SEASAFE (Slick Emissions And Ship Automatic Features Extraction), able to process SAR data in C-L-and X-bands. Moving from previous experience, new features have been added such as an enhanced land masking algorithm and oil spill characterization. Modules for ship detection and wind and wave evaluation are also available. The new improved system is here described; detection capabilities are evaluated, overall performances are described.

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A “dynamic” land masking algorithm for synthetic aperture radar images

Cited by 8 publications

References 9 publications

Ship Detection in Gaofen-3 SAR Images Based on Sea Clutter Distribution Analysis and Deep Convolutional Neural Network

Ship Detection in Gaofen-3 SAR Images Based on Sea Clutter Distribution Analysis and Deep Convolutional Neural Network

An Improved Method of Land Masking for Synthetic Aperture Radar-based Ship Detection

SEASAFE: an operational tool for sea surface monitoring with SAR

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