| WANG Changying,CHU Jialan,TAN Meng,SHAO Fengjing,SUI Yi,LI Shujing. 2017. An automatic detection of green tide using multi-windows with their adaptive threshold from Landsat TM/ETM plus image. Acta Oceanologica Sinica, 36(11):106－114 |
| An automatic detection of green tide using multi-windows with their adaptive threshold from Landsat TM/ETM plus image |
| 绿潮Lansat影像滑动窗口自适应阈值全自动检测方法 |
| Received:May 23, 2016 Revised:July 29, 2016 |
| DOI：10.1007/s13131-017-1141-9 |
| Key words:automatic detection green tide adaptive threshold Landsat TM/ETM plus image |
| 中文关键词: 自动检测 绿潮 自适应阈值 Landsat TM/ETM+影像 |
| 基金项目: |
| Author Name | Affiliation | E-mail | | WANG Changying | School of Data Science and Software Engineering, Qingdao University, Qingdao 266071, China
Institute of Big Data Technology and Smart City of Qingdao, Qingdao 266071, China
Key laboratory of Marine Red Tide Disaster Three-dimensional Monitoring Technology and Application, East China Sea Branch, State Oceanic Administration, Shanghai 200080, China | wcing80@126.com | | CHU Jialan | Key laboratory of Marine Red Tide Disaster Three-dimensional Monitoring Technology and Application, East China Sea Branch, State Oceanic Administration, Shanghai 200080, China
National Marine Environmental Monitoring Center, State Oceanic Administration, Dalian 116023, China | | | TAN Meng | North China Sea Data and Information Service Center, North China Sea Branch, State Oceanic Administration, Qingdao 266061, China | | | SHAO Fengjing | Institute of Big Data Technology and Smart City of Qingdao, Qingdao 266071, China | | | SUI Yi | Institute of Big Data Technology and Smart City of Qingdao, Qingdao 266071, China | | | LI Shujing | Institute of Big Data Technology and Smart City of Qingdao, Qingdao 266071, China | |
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| Abstract: |
| Since the atmospheric correction is a necessary preprocessing step of remote sensing image before detecting green tide, the introduced error directly affects the detection precision. Therefore, the detection method of green tide is presented from Landsat TM/ETM plus image which needs not the atmospheric correction. In order to achieve an automatic detection of green tide, a linear relationship (y=0.723x+0.504) between detection threshold y and subtraction x (x=λnir-λred) is found from the comparing Landsat TM/ETM plus image with the field surveys. Using this relationship, green tide patches can be detected automatically from Landsat TM/ETM plus image. Considering there is brightness difference between different regions in an image, the image will be divided into a plurality of windows (sub-images) with a same size firstly, and then each window will be detected using an adaptive detection threshold determined according to the discovered linear relationship. It is found that big errors will appear in some windows, such as those covered by clouds seriously. To solve this problem, the moving step k of windows is proposed to be less than the window width n. Using this mechanism, most pixels will be detected[n/k]×[n/k] times except the boundary pixels, then every pixel will be assigned the final class (green tide or sea water) according to majority rule voting strategy. It can be seen from the experiments, the proposed detection method using multi-windows and their adaptive thresholds can detect green tide from Landsat TM/ETM plus image automatically. Meanwhile, it avoids the reliance on the accurate atmospheric correction. |
| 中文摘要: |
| 大气校正是遥感影像绿潮检测之前必要的预处理步骤，大气校正所引入的误差直接影响绿潮检测结果的精度。为了消除大气校正给绿潮检测结果带来的误差，本文以Landsat影像为数据源，基于获取的绿潮爆发期影像与现场调查绿潮爆发范围的历史资料，分析得出Landsat影像中绿潮爆发区域与背景海水之间的光谱差异，发现绿潮与海水两者之间的分类阈值y与影像光谱差x=band （red）-band （nir）之间的存在线性关系y=0.723x+0.504，利用这一关系可实现Landsat影像绿潮自动检测；考虑到同一景影像不同区域之间存在亮度差异，本文将影像划分为多个同样大小的窗口，对每一窗口的自适应确定检测阈值，用以提高绿潮检测精度；实验发现，对于绿潮密度较大或云覆盖较严重的窗口区域，绿潮检测结果存在较大误差，虚警率较高，针对这一问题，本文提出窗口滑动步长k小于窗口宽度n的检测思路，对大部分检测点均会检测[n/k]*[n/k]次，最后采用投票的方式确定绿潮爆发区域。实验结果可以看出，本文提出的绿潮Landsat影像滑动窗口自适应阈值投票自动检测方法较传统的FAI和NDVI检测方法有所提高，而且避免了对大气校正处理精度的依赖。 |
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