Water area extraction using RADARSAT SAR imagery combined with Landsat imagery and terrain information.
Water area extraction using RADARSAT SAR imagery combined with Landsat imagery and terrain information.
复制标题
DOI：
10.3390/s150306652
复制
发表时间：
2015-03-19
期刊：
影响因子：
--
通讯作者：
Sohn HG
中科院分区：
文献类型：
--
作者：
Hong S;Jang H;Kim N;Sohn HG
本文探索了一种有效的利用SAR图像提取水体的方法，为不可预测的洪水情况下的洪水制图做准备。该方法基于阈值方法，利用合成孔径雷达的幅度、地形信息和基于目标的分类技术来去除噪声。由于合成孔径雷达图像中水域的幅值最小，利用合成孔径雷达幅值进行阈值分割可以有效地提取水体。然而，SAR图像中水域的反射特性不能区分陡峭地貌造成的遮挡区域，可以利用地形信息将其消除。尽管使用了利用SAR幅度和地形信息的阈值方法，但干扰用户解读水图的噪声仍然存在，噪声去除采用了基于目标大小准则的基于对象的分类，该准则是通过基于直方图的技术确定的。当仅利用SAR幅度信息时，总体准确率为83.67%。然而，利用合成孔径雷达的幅度、地形信息和去噪技术，在研究区进行分类的总体准确率为96.42%。特别是，用户准确率提高了46.00%。
This paper exploits an effective water extraction method using SAR imagery in preparation for flood mapping in unpredictable flood situations. The proposed method is based on the thresholding method using SAR amplitude, terrain information, and object-based classification techniques for noise removal. Since the water areas in SAR images have the lowest amplitude value, the thresholding method using SAR amplitude could effectively extract water bodies. However, the reflective properties of water areas in SAR imagery cannot distinguish the occluded areas caused by steep relief and they can be eliminated with terrain information. In spite of the thresholding method using SAR amplitude and terrain information, noises which interfered with users’ interpretation of water maps still remained and the object-based classification using an object size criterion was applied for the noise removal and the criterion was determined by a histogram-based technique. When only using SAR amplitude information, the overall accuracy was 83.67%. However, using SAR amplitude, terrain information and the noise removal technique, the overall classification accuracy over the study area turned out to be 96.42%. In particular, user accuracy was improved by 46.00%.
登录
查看更多内容
影响因子：
3.4
作者：
Brivio, PA;Colombo, R;Tomasoni, R
通讯作者：
Tomasoni, R
DOI：
10.1016/j.isprsjprs.2013.02.006
发表时间：
2013-05-01
影响因子：
12.7
作者：
Sebari, Imane;He, Dong-Chen
通讯作者：
He, Dong-Chen
影响因子：
8.2
作者：
VANZYL, JJ;CHAPMAN, BD;SHI, JC
通讯作者：
SHI, JC
DOI：
10.1109/tgrs.2009.2029236
发表时间：
2010-02-01
影响因子：
8.2
作者：
Mason, David C.;Speck, Rainer;Bates, Paul D.
通讯作者：
Bates, Paul D.
影响因子：
13.5
作者：
Birkett, CM
通讯作者：
Birkett, CM