Dr. Xianwen Ding Profile

Dr. Xianwen Ding

Instructor at Shanghai Ocean Univ

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Publications (6)

Proceedings Article | 19 February 2018 Paper

Segmentation of oil spills in SAR images by using discriminant cuts

Xianwen Ding, Xiaolin Zou

Proceedings Volume 10608, 106080L (2018) https://doi.org/10.1117/12.2287314

KEYWORDS: Image segmentation, Synthetic aperture radar, Image classification, Fuzzy logic, Anisotropic diffusion

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Proceedings Article | 14 December 2015 Paper

Spot detection from MODIS imagery using 2P-CFAR

Xianwen Ding, Xiaofeng Li

Proceedings Volume 9815, 98151E (2015) https://doi.org/10.1117/12.2204947

KEYWORDS: MODIS, Sensors, Atmospheric modeling, Image processing, Image enhancement, Clouds, Ocean optics, Remote sensing, Target detection, Neodymium

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Oil spills are one of the major environmental concerns, especially in the coastal zones of the ocean. Satellite remote sensing imagery has proved to be a useful tool for monitoring oil spills in the marine environment. With its two daily acquisitions and the possibility to obtain near-real-time data free of charge, the Moderate Resolution Imaging Spectroradiometer (MODIS) shows interesting potential as such a cost-effective supplementary tool. Several researches on oil spill detection in MODIS imagery has been carried out for the past few years. Basically, oil spills were manually detected from MODIS imagery [1,2]. The disadvantage of the manual detection method is inefficient and subjective. Shi et al. proposed an oil spill detection method from MODIS imagery by using fuzzy cluster and texture feature extraction [3]. It works in an automatic manner and does not require any priori knowledge of occurrence or the spectral attributes of spills. But its efficiency in very near shore regions is limited. Chen and Zhao detected oil spills from the oil-water contrast ratio image by using a thresholding method [4].They found that the oil-water contrast ratio can be enhanced by replacing the original image with the ratio image of two different band ones in 400-800 nm. To obtain the oil-water contrast ratio image from the MODIS imagery, they selected the oil spill area and the background sea area and then calculated the mean radiance or emissivity value in those areas. By doing so, the automation and the accuracy of the method were reduced. Adamo et al. [5] and Kudryavtsev et al. [6] proposed physical methods for oil spill detection from MODIS imagery acquired in sunglint conditions. These two methods take imaging geometry into consideration and have the aid of other models or functions such as the Cox and Munk (1954) model [7],the CMOD4 model [8,9], the ECMWF (European Centre for Medium-Range Weather Forecasts) atmospheric model, and the transfer function, which increase the algorithm complexity and rely on some assumptions.

Proceedings Article | 26 October 2013 Paper

Fusion of SAR and MODIS images for oceanic internal waves tracking in the South China Sea

Bingqing Liu, Hong Yang, Xianwen Ding, Zhongxiang Zhao, Xiaofeng Li

Proceedings Volume 8921, 89210L (2013) https://doi.org/10.1117/12.2035676

KEYWORDS: MODIS, Image fusion, Synthetic aperture radar, Wave propagation, Satellite imaging, Geographic information systems, Satellites, Phase measurement, Remote sensing, Earth observing sensors

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Proceedings Article | 8 December 2011 Paper

A novel method to suppress noise in marine radar images based on pulse-pulse correlation

Xianwen Ding, Peng Chen, Shengqi He, Zongsheng Zheng

Proceedings Volume 8002, 800211 (2011) https://doi.org/10.1117/12.901651

KEYWORDS: Radar, Ocean optics, Digital filtering, Signal to noise ratio, Image segmentation, X band, Image filtering, Image processing, Electronic filtering, Electromagnetic radiation

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Proceedings Article | 30 October 2009 Paper

Ship detection in presence of sea clutter from temporal sequences of navigation radar images

Xianwen Ding, Weigen Huang, Changbao Zhou, Peng Chen, Bin Liu

Proceedings Volume 7495, 74954I (2009) https://doi.org/10.1117/12.832882

KEYWORDS: Radar, Target detection, X band, Detection and tracking algorithms, Neural networks, Image processing, Floods, Binary data, Oceanography, Sensors

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Showing 5 of 6 publications

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