Satellite Derived Bathymetry Using Machine Learning and Multi-Temporal Satellite Images

Remote Sensing

卷:11

Yuta Yamashita¹  Tsutomu Yamanokuchi²  Tatsuyuki Sagawa²  Toshio Okumura² 

[1] Bestmateria, 2-43-15 Misawa, Hino-shi, Tokyo 191-0032, Japan;

[2] Remote Sensing Technology Center of Japan, Tokyu Reit Toranomon Bldg. 3F, 3-17-1 Toranomon, Minato-ku, Tokyo 105-0001, Japan;

关键词: satellite derived bathymetry;    shallow water;    machine learning;    random forest;    Google Earth Engine;    multi-temporal;    Landsat-8;   

DOI  :  10.3390/rs11101155

来源: DOAJ

【 摘 要 】

Shallow water bathymetry is important for nautical navigation to avoid stranding, as well as for the scientific simulation of high tide and high waves in coastal areas. Although many studies have been conducted on satellite derived bathymetry (SDB), previously used methods basically require supervised data for analysis, and cannot be used to analyze areas that are unreachable by boat or airplane. In this study, a mapping method for shallow water bathymetry was developed, using random forest machine learning and multi-temporal satellite images to create a generalized depth estimation model. A total of 135 Landsat-8 images, and a large amount of training bathymetry data for five areas were analyzed with the Google Earth Engine. The accuracy of SDB was evaluated by comparison with reference bathymetry data. The root mean square error in the final estimated water depth in the five test areas was 1.41 m for depths of 0 to 20 m. The SDB creation system developed in this study is expected to be applicable in various shallow water regions under highly transparent conditions.

【 授权许可】

Unknown