RESUMEN
The method described attempts to remotely identify the shape of an anthropogenic object, such as a wreck of a modern vessel, using reflectivity data from Multi-Beam Echosounder (MBES) systems. In the beam domain, the backscatter strength values - geometrically and radiometrically corrected - are used to extract a large number of Gray Level Co-occurrence Matrix (GLCM) features with different input parameters. Principal Component Analysis (PCA) is applied in order to achieve dimensionality reduction whilst a K-means algorithm clusters as "shipwreck site" a large number of beams for each line. After the geo-referencing process, a K-nearest-neighbors (K-NN) technique is applied as a filter for possible misclassifications. Finally, the shape of the shipwreck site is defined from the georeferenced beams using the α-shape method, constructing an output compatible with Geographic Information Systems (GIS).
Asunto(s)
Técnicas de Apoyo para la Decisión , Tecnología de Sensores Remotos/métodos , Navíos , Accidentes , Algoritmos , Colombia Británica , Análisis por Conglomerados , Sistemas de Información Geográfica , Océano Pacífico , Análisis de Componente Principal , Tecnología de Sensores Remotos/instrumentaciónRESUMEN
Bathymetry (seafloor depth), is a critical parameter providing the geospatial context for a multitude of marine scientific studies. Since 1997, the International Bathymetric Chart of the Arctic Ocean (IBCAO) has been the authoritative source of bathymetry for the Arctic Ocean. IBCAO has merged its efforts with the Nippon Foundation-GEBCO-Seabed 2030 Project, with the goal of mapping all of the oceans by 2030. Here we present the latest version (IBCAO Ver. 4.0), with more than twice the resolution (200 × 200 m versus 500 × 500 m) and with individual depth soundings constraining three times more area of the Arctic Ocean (â¼19.8% versus 6.7%), than the previous IBCAO Ver. 3.0 released in 2012. Modern multibeam bathymetry comprises â¼14.3% in Ver. 4.0 compared to â¼5.4% in Ver. 3.0. Thus, the new IBCAO Ver. 4.0 has substantially more seafloor morphological information that offers new insights into a range of submarine features and processes; for example, the improved portrayal of Greenland fjords better serves predictive modelling of the fate of the Greenland Ice Sheet.