Quantitative Assessment for Detection and Monitoring of Coastline Dynamics with Temporal RADARSAT Images
Quantitative Assessment for Detection and Monitoring of Coastline Dynamics with Temporal RADARSAT Images
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DOI：
10.3390/rs10111705
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发表时间：
2018-10
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影响因子：
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通讯作者：
B. Pradhan;Hossein Mojaddadi Rizeei;Abdinur Abdulle
中科院分区：
文献类型：
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作者：
B. Pradhan;Hossein Mojaddadi Rizeei;Abdinur Abdulle
本研究旨在利用时序合成孔径雷达（SAR）图像探测马来西亚吉兰丹州的海岸线变化。使用2003年捕获的RADARSAT-1和2014年捕获的RADARSAT-2两幅活动图像来监测这种变化。我们对RADARSAT图像进行去噪和边缘检测滤波预处理，去除盐和胡椒畸变。对滤波后的图像进行了不同的分割分析。首先，采用多分辨率分割、最大光谱差和棋盘分割等方法分离陆地像元和海洋像元；其次，采用田口法对分割参数进行优化。随后，在优化后的片段上应用支持向量机算法对海岸线进行分类，两幅时间图像的准确率均达到98%。使用马来西亚测绘部的专题地图对结果进行了验证。变化检测显示，2003 - 2014年的平均海岸线差异为12.5 m。本研究中开发的方法证明了主动SAR传感器绘制和探测海岸线变化的能力，特别是在热带地区的低潮或涨潮期间，被动传感器图像经常被云层掩盖。
This study aims to detect coastline changes using temporal synthetic aperture radar (SAR) images for the state of Kelantan, Malaysia. Two active images, namely, RADARSAT-1 captured in 2003 and RADARSAT-2 captured in 2014, were used to monitor such changes. We applied noise removal and edge detection filtering on RADARSAT images for preprocessing to remove salt and pepper distortion. Different segmentation analyses were also applied to the filtered images. Firstly, multiresolution segmentation, maximum spectral difference and chessboard segmentation were performed to separate land pixels from ocean ones. Next, the Taguchi method was used to optimise segmentation parameters. Subsequently, a support vector machine algorithm was applied on the optimised segments to classify shorelines with an accuracy of 98% for both temporal images. Results were validated using a thematic map from the Department of Survey and Mapping of Malaysia. The change detection showed an average difference in the shoreline of 12.5 m between 2003 and 2014. The methods developed in this study demonstrate the ability of active SAR sensors to map and detect shoreline changes, especially during low or high tides in tropical regions where passive sensor imagery is often masked by clouds.