The use of linear smoothing methods to remove artefacts resulting from the seabed’s DTM lossy compression

Maleika, Wojciech

doi:10.1007/s12518-022-00427-1

Cited by 1 publication

(1 citation statement)

References 24 publications

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“…The resulting high-accuracy surface smoothing method (HASM-SM) efficiently filters noise in grid-based surfaces like DEMs. An interesting solution was presented in [83], utilising local linear smoothing methods to remove artefacts from lossy compression of a digital model of the sea floor. In work [84], an algorithm of the multiquadric method (MQ) based on an improved Huber loss function (MQ-IH) was developed.…”

Section: Introductionmentioning

confidence: 99%

Application of Filtering Techniques to Smooth a Surface of Hybrid Digital Bathymetric Model

Lubczonek,

Zaniewicz

2023

Remote Sensing

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The aim of the research is to identify the optimal method for smoothing the surface of a hybrid digital bathymetric model (HDBM). The initiation of this research is justified by the fact that a model created from diverse types of data may have different surface textures and outliers. This diversity may cause problems in subsequent data processing stages, such as generating depth contours. As part of the adopted research methodology, fifteen filters were analysed. Filtering techniques were examined for filter type, the number of iterations, weights, and window size. The result is the adopted research methodology, which enabled the selection of the optimal filtering method. The research undertaken in this work is an extension of the methodology for developing an HDBM. An important aspect of the research is the approach to elaborating on such kinds of models in shallow and ultra-shallow waters adjacent to the land, as well as the use of data obtained by modern measurement platforms, such as unmanned surface vehicles (USV) and unmanned aerial vehicles (UAV). The studies fit into the general context of works related to the development of this type of model and undoubtedly provide a solid reference for further development or improvement of similar methods.

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Section: Introductionmentioning

confidence: 99%