|Title:||High Resolution Satellite Bathymetry Mapping: Regression and Machine Learning Based Approaches||Authors:||Eugenio González, Francisco
Marcello Ruiz, Francisco Javier
Mederos Barrera, Antonio Ramón
|UNESCO Clasification:||220990 Tratamiento digital. Imágenes||Keywords:||Atmospheric modeling
Biological system modeling
Multispectral WorldView-2/3, et al
|Issue Date:||2021||Project:||Procesado Avanzado de Datos de Teledetección Para la Monitorización y Gestión Sostenible de Recursos Marinos y Terrestres en Ecosistemas Vulnerables.
|Journal:||IEEE Transactions on Geoscience and Remote Sensing||Abstract:||Remote spectral imaging of coastal areas can provide valuable information for their sustainable management and conservation of their biodiversity. Unfortunately, such areas are very sensitive to changes due to human activity, natural phenomenona, introduction of non-native species and climate change. Thus, the main objective of this research is the implementation of a robust image processing methodology to produce accurate bathymetry maps in shallow coastal waters using high resolution multispectral WorldView-2/3 satellite imagery for the monitoring at the maximum spatial and spectral resolutions. Two different island ecosystems have been selected for the assessment, since they stand out for their richness in endemic species and they are more vulnerable to climate change: Cabrera National Park and Maspalomas Natural Protected area, located in the Balearic and Canary Islands, Spain, respectively. In addition, a third example to show the applicability of the mapping methodology to monitor the construction of a new port in Granadilla (Canary Islands) is presented. Contributions of this work focus on improving the preprocessing methodology and, mainly, on the proposal and assessment of new satellite derived regression and machine learning bathymetric models, which have been validated and compared with respect to measured reference bathymetry. After a thorough analysis of 9 techniques, using visual and quantitative statistical parameters, ensemble learning approaches have demonstrated excellent performance, even in challenging scenarios up to 35 m depth, with mean RMSE values around 2 m.||URI:||http://hdl.handle.net/10553/114111||ISSN:||0196-2892||DOI:||10.1109/TGRS.2021.3135462||Source:||IEEE Transactions on Geoscience and Remote Sensing [ISSN 0196-2892], 14 diciembre 2021|
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