M. D. Manessa, A. Kanno, M. Sekine, Muhammad Haidar, Koichi Yamamoto, T. Imai, T. higuchi
{"title":"使用随机森林算法和世界观-2图像的卫星衍生测深","authors":"M. D. Manessa, A. Kanno, M. Sekine, Muhammad Haidar, Koichi Yamamoto, T. Imai, T. higuchi","doi":"10.14710/GEOPLANNING.3.2.117-126","DOIUrl":null,"url":null,"abstract":"In empirical approach, the satellite-derived bathymetry (SDB) is usually derived from a linear regression. However, the depth variable in surface reflectance has a more complex relation. In this paper, a methodology was introduced using a nonlinear regression of Random Forest (RF) algorithm for SDB in shallow coral reef water. Worldview-2 satellite images and water depth measurement samples using single beam echo sounder were utilized. Furthermore, the surface reflectance of six visible bands and their logarithms were used as an input in RF and then compared with conventional methods of Multiple Linear Regression (MLR) at ten times cross validation. Moreover, the performance of each possible pair from six visible bands was also tested. Then, the estimated depth from two methods and each possible pairs were evaluated in two sites in Indonesia: Gili Mantra Island and Panggang Island, using the measured bathymetry data. As a result, for the case of all bands used the RF in compared with MLR showed better fitting ensemble, -0.14 and -1.27m of RMSE and 0.16 and 0.47 of R 2 improvement for Gili Mantra Islands and Panggang Island, respectively. Therefore, the RF algorithm demonstrated better performance and accuracy compared with the conventional method. While for best pair identification, all bands pair wound did not give the best result. Surprisingly, the usage of green, yellow, and red bands showed good water depth estimation accuracy.","PeriodicalId":30789,"journal":{"name":"Geoplanning Journal of Geomatics and Planning","volume":"3 1","pages":"117-126"},"PeriodicalIF":0.0000,"publicationDate":"2016-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.14710/GEOPLANNING.3.2.117-126","citationCount":"49","resultStr":"{\"title\":\"SATELLITE-DERIVED BATHYMETRY USING RANDOM FOREST ALGORITHM AND WORLDVIEW-2 IMAGERY\",\"authors\":\"M. D. Manessa, A. Kanno, M. Sekine, Muhammad Haidar, Koichi Yamamoto, T. Imai, T. higuchi\",\"doi\":\"10.14710/GEOPLANNING.3.2.117-126\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In empirical approach, the satellite-derived bathymetry (SDB) is usually derived from a linear regression. However, the depth variable in surface reflectance has a more complex relation. In this paper, a methodology was introduced using a nonlinear regression of Random Forest (RF) algorithm for SDB in shallow coral reef water. Worldview-2 satellite images and water depth measurement samples using single beam echo sounder were utilized. Furthermore, the surface reflectance of six visible bands and their logarithms were used as an input in RF and then compared with conventional methods of Multiple Linear Regression (MLR) at ten times cross validation. Moreover, the performance of each possible pair from six visible bands was also tested. Then, the estimated depth from two methods and each possible pairs were evaluated in two sites in Indonesia: Gili Mantra Island and Panggang Island, using the measured bathymetry data. As a result, for the case of all bands used the RF in compared with MLR showed better fitting ensemble, -0.14 and -1.27m of RMSE and 0.16 and 0.47 of R 2 improvement for Gili Mantra Islands and Panggang Island, respectively. Therefore, the RF algorithm demonstrated better performance and accuracy compared with the conventional method. While for best pair identification, all bands pair wound did not give the best result. Surprisingly, the usage of green, yellow, and red bands showed good water depth estimation accuracy.\",\"PeriodicalId\":30789,\"journal\":{\"name\":\"Geoplanning Journal of Geomatics and Planning\",\"volume\":\"3 1\",\"pages\":\"117-126\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-10-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.14710/GEOPLANNING.3.2.117-126\",\"citationCount\":\"49\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Geoplanning Journal of Geomatics and Planning\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.14710/GEOPLANNING.3.2.117-126\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"Social Sciences\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Geoplanning Journal of Geomatics and Planning","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.14710/GEOPLANNING.3.2.117-126","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Social Sciences","Score":null,"Total":0}
SATELLITE-DERIVED BATHYMETRY USING RANDOM FOREST ALGORITHM AND WORLDVIEW-2 IMAGERY
In empirical approach, the satellite-derived bathymetry (SDB) is usually derived from a linear regression. However, the depth variable in surface reflectance has a more complex relation. In this paper, a methodology was introduced using a nonlinear regression of Random Forest (RF) algorithm for SDB in shallow coral reef water. Worldview-2 satellite images and water depth measurement samples using single beam echo sounder were utilized. Furthermore, the surface reflectance of six visible bands and their logarithms were used as an input in RF and then compared with conventional methods of Multiple Linear Regression (MLR) at ten times cross validation. Moreover, the performance of each possible pair from six visible bands was also tested. Then, the estimated depth from two methods and each possible pairs were evaluated in two sites in Indonesia: Gili Mantra Island and Panggang Island, using the measured bathymetry data. As a result, for the case of all bands used the RF in compared with MLR showed better fitting ensemble, -0.14 and -1.27m of RMSE and 0.16 and 0.47 of R 2 improvement for Gili Mantra Islands and Panggang Island, respectively. Therefore, the RF algorithm demonstrated better performance and accuracy compared with the conventional method. While for best pair identification, all bands pair wound did not give the best result. Surprisingly, the usage of green, yellow, and red bands showed good water depth estimation accuracy.