scholarly journals SPAN: Strong Scattering Point Aware Network for Ship Detection and Classification in Large-Scale SAR Imagery

2022 ◽  
pp. 1-1
Author(s):  
Yuanrui Sun ◽  
Zhirui Wang ◽  
Xian Sun ◽  
Kun Fu
Keyword(s):  
Large Scale ◽  
Ship Detection ◽  
Sar Imagery ◽  
Strong Scattering

scholarly journals ESTIMATION OF DAMAGED AREAS DUE TO THE 2010 CHILE EARTHQUAKE AND TSUNAMI USING SAR IMAGERY OF ALOS/PALSAR

2016 ◽  
Vol III-8 ◽  
pp. 35-42
Author(s):  
Pertiwi Jaya Ni Made ◽  
Fusanori Miura ◽  
A. Besse Rimba
Keyword(s):  
Large Scale ◽  
Aerial Photographs ◽  
Alos Palsar ◽  
Aster Gdem ◽  
Tsunami Damage ◽  
Before And After ◽  
Chile Earthquake ◽  
Multi Temporal ◽  
Sar Imagery ◽  
Inundation Map

A large-scale earthquake and tsunami affect thousands of people and cause serious damages worldwide every year. Quick observation of the disaster damage is extremely important for planning effective rescue operations. In the past, acquiring damage information was limited to only field surveys or using aerial photographs. In the last decade, space-borne images were used in many disaster researches, such as tsunami damage detection. In this study, SAR data of ALOS/PALSAR satellite images were used to estimate tsunami damage in the form of inundation areas in Talcahuano, the area near the epicentre of the 2010 Chile earthquake. The image processing consisted of three stages, i.e. pre-processing, analysis processing, and post-processing. It was conducted using multi-temporal images before and after the disaster. In the analysis processing, inundation areas were extracted through the masking processing. It consisted of water masking using a high-resolution optical image of ALOS/AVNIR-2 and elevation masking which built upon the inundation height using DEM image of ASTER-GDEM. The area result was 8.77 Km<sup>2</sup>. It showed a good result and corresponded to the inundation map of Talcahuano. Future study in another area is needed in order to strengthen the estimation processing method.

scholarly journals Large-Scale Automatic Vessel Monitoring Based on Dual-Polarization Sentinel-1 and AIS Data

2019 ◽  
Vol 11 (9) ◽  
pp. 1078 ◽  
Author(s):  
Ramona Pelich ◽  
Marco Chini ◽  
Renaud Hostache ◽  
Patrick Matgen ◽  
Carlos Lopez-Martinez ◽  
...  
Keyword(s):  
Detection Rate ◽  
Large Scale ◽  
Added Value ◽  
Automatic Identification ◽  
Identification System ◽  
Dual Polarization ◽  
Ship Detection ◽  
Traffic Conditions ◽  
Intensity Images ◽  
Polarization Channel

This research addresses the use of dual-polarimetric descriptors for automatic large-scale ship detection and characterization from synthetic aperture radar (SAR) data. Ship detection is usually performed independently on each polarization channel and the detection results are merged subsequently. In this study, we propose to make use of the complex coherence between the two polarization channels of Sentinel-1 and to perform vessel detection in this domain. Therefore, an automatic algorithm, based on the dual-polarization coherence, and applicable to entire large scale SAR scenes in a timely manner, is developed. Automatic identification system (AIS) data are used for an extensive and also large scale cross-comparison with the SAR-based detections. The comparative assessment allows us to evaluate the added-value of the dual-polarization complex coherence, with respect to SAR intensity images in ship detection, as well as the SAR detection performances depending on a vessel’s size. The proposed methodology is justified statistically and tested on Sentinel-1 data acquired over two different and contrasting, in terms of traffic conditions, areas: the English Channel the and Pacific coastline of Mexico. The results indicate a very high SAR detection rate, i.e., >80%, for vessels larger than 60 m and a decrease of detection rate up to 40 % for smaller size vessels. In addition, the analysis highlights many SAR detections without corresponding AIS positions, indicating the complementarity of SAR with respect to cooperative sources for detecting dark vessels.

A waterborne salient ship detection method on SAR imagery

2015 ◽  
Vol 58 (8) ◽  
pp. 1-3 ◽  
Author(s):  
Long Ma ◽  
Liang Chen ◽  
XueJing Zhang ◽  
He Chen ◽  
Nouman Qadeer Soomro
Keyword(s):  
Detection Method ◽  
Ship Detection ◽  
Sar Imagery

scholarly journals Ship detection in SAR imagery: A comparison study

2017 ◽  
Author(s):  
Pasquale Iervolino ◽  
Raffaella Guida ◽  
Parivash Lumsdon ◽  
Jurgen Janoth ◽  
Melanie Clift ◽  
...  
Keyword(s):  
Comparison Study ◽  
Ship Detection ◽  
Sar Imagery
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