scholarly journals Oil Spill Detection from RADARSAT-2 SAR Image Using Non-Local Means Filter

2017 ◽  
Vol 33 (1) ◽  
pp. 61-67 ◽  
Author(s):  
Daeseong Kim ◽  
Hyung-Sup Jung
Keyword(s):  
Oil Spill ◽  
Sar Image ◽  
Oil Spill Detection ◽  
Local Means ◽  
Non Local

scholarly journals Synthetic-Aperture Radar Image Despeckling based on Improved Non-Local Means and Non-Subsampled Shearlet Transform

2020 ◽  
Vol 49 (3) ◽  
pp. 299-307
Author(s):  
Zengguo Sun ◽  
Rui Shi ◽  
Wei Wei
Keyword(s):  
Synthetic Aperture Radar Image ◽  
Synthetic Aperture ◽  
Sar Image ◽  
Sar Images ◽  
Shearlet Transform ◽  
Local Means ◽  
New Type ◽  
Non Local ◽  
Homogeneous Regions ◽  
Transform Domains

When Synthetic-Aperture (SAR) image is transformed into wavelet domain and other transform domains, most of the coefficients of the image are small or zero. This shows that SAR image is sparse. However, speckle can be seen in SAR images. The non-local means is a despeckling algorithm, but it cannot overcome the speckle in homogeneous regions and it blurs edge details of the image. In order to solve these problems, an improved non-local means is suggested. At the same time, in order to better suppress the speckle effectively in edge regions, the non-subsampled Shearlet transform (NSST) is applied. By combining NSST with the improved non-local means, a new type of despeckling algorithm is proposed. Results show that the proposed algorithm leads to a satisfying performance for SAR images.

One Operational Visual Method for Oil Spill Detection by SAR Image

2015 ◽  
Vol 742 ◽  
pp. 208-211
Author(s):  
Peng Chen ◽  
Kai Guo Fan ◽  
Yan Zhen Gu ◽  
Ke Xu
Keyword(s):  
Oil Spill ◽  
Marine Environment ◽  
Sar Image ◽  
Environment Protection ◽  
Visual Method ◽  
Oil Spill Detection

SAR has been proven to be a useful tool for ocean oil spill detection due to its large coverage, independence of the day and night cycle and all-weather capability. In this paper, one operational visual method for oil spill detection using SAR image was performed and the oil spill key information, such as the location and coverage, has also been demonstrated. The results show that the operational visual method of oil spill detection by SAR image will play an important role in the marine environment protection.

scholarly journals Bankline detection of GF-3 SAR images based on shearlet

2021 ◽  
Vol 7 ◽  
pp. e611
Author(s):  
Zengguo Sun ◽  
Guodong Zhao ◽  
Marcin Woźniak ◽  
Rafał Scherer ◽  
Robertas Damaševičius
Keyword(s):  
Water Environment ◽  
Detection Algorithm ◽  
Morphological Processing ◽  
Map Matching ◽  
Sar Image ◽  
Sar Images ◽  
Multi Scale ◽  
Local Means ◽  
Active Imaging ◽  
Non Local

The GF-3 satellite is China’s first self-developed active imaging C-band multi-polarization synthetic aperture radar (SAR) satellite with complete intellectual property rights, which is widely used in various fields. Among them, the detection and recognition of banklines of GF-3 SAR image has very important application value for map matching, ship navigation, water environment monitoring and other fields. However, due to the coherent imaging mechanism, the GF-3 SAR image has obvious speckle, which affects the interpretation of the image seriously. Based on the excellent multi-scale, directionality and the optimal sparsity of the shearlet, a bankline detection algorithm based on shearlet is proposed. Firstly, we use non-local means filter to preprocess GF-3 SAR image, so as to reduce the interference of speckle on bankline detection. Secondly, shearlet is used to detect the bankline of the image. Finally, morphological processing is used to refine the bankline and further eliminate the false bankline caused by the speckle, so as to obtain the ideal bankline detection results. Experimental results show that the proposed method can effectively overcome the interference of speckle, and can detect the bankline information of GF-3 SAR image completely and smoothly.

Oil Spill Detection Based on CBD-Net Using Marine SAR Image

2021 ◽  
Author(s):  
Yanan Zhang ◽  
Qiqi Zhu ◽  
Qingfeng Guan
Keyword(s):  
Oil Spill ◽  
Sar Image ◽  
Oil Spill Detection
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