Geomatics (Basel, Switzerland)最新文献

筛选

英文中文

Ensemble Machine Learning Approaches for Bathymetry Estimation in Multi-Spectral Images. 多光谱图像测深估计的集成机器学习方法。

Geomatics (Basel, Switzerland) Pub Date : 2025-09-01 Epub Date: 2025-07-22 DOI: 10.3390/geomatics5030034

Kazi Aminul Islam, Omar Abul-Hassan, Hongfang Zhang, Victoria Hill, Blake Schaeffer, Richard Zimmerman, Jiang Li

{"title":"Ensemble Machine Learning Approaches for Bathymetry Estimation in Multi-Spectral Images.","authors":"Kazi Aminul Islam, Omar Abul-Hassan, Hongfang Zhang, Victoria Hill, Blake Schaeffer, Richard Zimmerman, Jiang Li","doi":"10.3390/geomatics5030034","DOIUrl":"10.3390/geomatics5030034","url":null,"abstract":"Traditional bathymetry measures require a large number of human hours, and many bathymetry records are obsolete or missing. Automated measures of bathymetry would reduce costs and increase accessibility for research and applications. In this paper, we optimized a recent machine learning model, named CatBoostOpt, to estimate bathymetry based on high-resolution WorldView-2 (WV-2) multi-spectral optical satellite images. Cat-BoostOpt was demonstrated across the Florida Big Bend coastline, where the model learned correlations between in situ sound Navigation and Ranging (Sonar) bathymetry measurements and the corresponding multi-spectral reflectance values in WV-2 images to map bathymetry. We evaluated three different feature transformations as inputs for bathymetry estimation, including raw reflectance, log-linear, and log-ratio transforms of the raw reflectance value in WV-2 images. In addition, we investigated the contribution of each spectral band and found that utilizing all eight spectral bands in WV-2 images offers the best solution for handling complex water quality conditions. We compared CatBoostOpt with linear regression (LR), support vector machine (SVM), random forest (RF), AdaBoost, gradient boosting, and deep convolutional neural network (DCNN). CatBoostOpt with log-ratio transformed reflectance achieved the best performance with an average root mean square error (RMSE) of 0.34 and coefficient of determination (R2) of 0.87.","PeriodicalId":521046,"journal":{"name":"Geomatics (Basel, Switzerland)","volume":"5 3","pages":"34"},"PeriodicalIF":0.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12425137/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145067584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0