A coastline is defined as the average spring tide line. Different types of seacoast, such as sandy, silty, and bio- logical coast, have different indicators of interpretation. It is very difficult to develop a univers...A coastline is defined as the average spring tide line. Different types of seacoast, such as sandy, silty, and bio- logical coast, have different indicators of interpretation. It is very difficult to develop a universal method for interpreting all shorelines. Therefore, the sandy, the silty, and the biological coast are regarded as research objects, and with data mining technolog,found the rules of interpretation of those three types of coastlines. Then, an intelligent coastline interpretation method based on rules was proposed. Firstly, the rules for ex- tracting the waterline in Landsat TM/ETM＋ （Thematic Mapper/Enhanced Thematic Mapper Plus） imagery were discovered. Then, through analyzing the features of sandy, silty and biological coast, the indicators of interpreting different types of shoreline were determined. According to the indicators, the waterline could be corrected to the real coastline. In order to verify the validity of the proposed algorithms, three Landsat TM/ETM＋ imageries were selected for case studies. The experimental results showed that the proposed methods could interpret the coastlines of sandy; silty, and biological coasts with high precision and without human intervention, which exceeded three pixels.展开更多
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...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 =0.723 x＋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.展开更多
基金The Science and Technology Development Plan Projects of Shandong Province of China under contract No.2011YD15005the National Natural Science Foundation of China under contact Nos 91130035 and 40906094
文摘A coastline is defined as the average spring tide line. Different types of seacoast, such as sandy, silty, and bio- logical coast, have different indicators of interpretation. It is very difficult to develop a universal method for interpreting all shorelines. Therefore, the sandy, the silty, and the biological coast are regarded as research objects, and with data mining technolog,found the rules of interpretation of those three types of coastlines. Then, an intelligent coastline interpretation method based on rules was proposed. Firstly, the rules for ex- tracting the waterline in Landsat TM/ETM＋ （Thematic Mapper/Enhanced Thematic Mapper Plus） imagery were discovered. Then, through analyzing the features of sandy, silty and biological coast, the indicators of interpreting different types of shoreline were determined. According to the indicators, the waterline could be corrected to the real coastline. In order to verify the validity of the proposed algorithms, three Landsat TM/ETM＋ imageries were selected for case studies. The experimental results showed that the proposed methods could interpret the coastlines of sandy; silty, and biological coasts with high precision and without human intervention, which exceeded three pixels.
基金The National Natural Science Foundation of China under contract Nos 41506198 and 41476101the Natural Science Foundation Projects of Shandong Province of China under contract No.ZR2012FZ003the Science and Technology Development Plan of Qingdao City of China under contract No.13-1-4-121-jch
文摘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 =0.723 x＋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.
