Smoothing Via Iterative Averaging (SIA)A Basic Technique for Line Smoothing

Mansouryar, Mohsen; Hedayati, Amin

doi:10.7763/ijcee.2012.v4.501

Cited by 11 publications

(11 citation statements)

References 9 publications

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“…The LCPA results in a line shape, which angular shaped due to raster cells. Thus, we smoothed the resulting line shape for illustrative purposes via iterative averaging [ 42 , 43 ].…”

Section: Methodsmentioning

confidence: 99%

Shaping pre-modern digital terrain models: The former topography at Charlemagne’s canal construction site

2018

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The use of remote sensing techniques to identify (geo)archaeological features is wide spread. For archaeological prospection and geomorphological mapping, Digital Terrain Models (DTMs) on based LiDAR (Light Detection And Ranging) are mainly used to detect surface and subsurface features. LiDAR is a remote sensing tool that scans the surface with high spatial resolution and allows for the removal of vegetation cover with special data filters. Archaeological publications with LiDAR data in issues have been rising exponentially since the mid-2000s. The methodology of DTM analyses within geoarchaeological contexts is usually based on “bare-earth” LiDAR data, although the terrain is often significantly affected by human activities. However, “bare-earth” LiDAR data analyses are very restricted in the case of historic hydro-engineering such as irrigation systems, mills, or canals because modern roads, railway tracks, buildings, and earth lynchets influence surface water flows and may dissect the terrain. Consequently, a "natural" pre-modern DTM with high depth accuracy is required for palaeohydrological analyses. In this study, we present a GIS-based modelling approach to generate a pre-modern and topographically purged DTM. The case study focuses on the landscape around the Early Medieval Fossa Carolina, a canal constructed by Charlemagne and one of the major medieval engineering projects in Europe. Our aim is to reconstruct the pre-modern relief around the Fossa Carolina for a better understanding and interpretation of the alignment of the Carolingian canal. Our input data are LiDAR-derived DTMs and a comprehensive vector layer of anthropogenic structures that affect the modern relief. We interpolated the residual points with a spline algorithm and smoothed the result with a low pass filter. The purged DTM reflects the pre-modern shape of the landscape. To validate and ground-truth the model, we used the levels of recovered pre-modern soils and surfaces that have been buried by floodplain deposits, colluvial layers, or dam material of the Carolingian canal. We compared pre-modern soil and surface levels with the modelled pre-modern terrain levels and calculated the overall error. The modelled pre-modern surface fits with the levels of the buried soils and surfaces. Furthermore, the pre-modern DTM allows us to model the most favourable course of the canal with minimal earth volume to dig out. This modelled pathway corresponds significantly with the alignment of the Carolingian canal. Our method offers various new opportunities for geoarchaeological terrain analysis, for which an undisturbed high-precision pre-modern surface is crucial.

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“…The LCPA results in a line shape, which angular shaped due to raster cells. Thus, we smoothed the resulting line shape for illustrative purposes via iterative averaging [ 42 , 43 ].…”

Section: Methodsmentioning

confidence: 99%

Shaping pre-modern digital terrain models: The former topography at Charlemagne’s canal construction site

2018

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“…Lawford also studied the relationship between simplification (level of detail) and several scales. Mansouryar and Hedayati [17] applied an iterative process of line smoothing which is categorized as an averaging method.…”

Section: Theoretical Perceptions and Examples Of Simplification Usagementioning

confidence: 99%

Preliminary Analysis of Quality of Contour Lines Using Smoothing Algorithms

et al. 2019

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In this paper several well-known filtering techniques were compared in the purpose of automatic line generalization. The used methods for line simplification are digital first order low-pass filter, Savitzky-Golay (SG) filter and Whittaker filter. Two versions of the algorithm for line feature generalization were tested, from source scale 1:25 000 towards target scale of 1:100 000 and from source scale 1:25 000 towards scale of 1:50 000. Also, GPS data filtering for the target scale 1:50 000 was tested. The first version of the algorithm considers that there are no control data, and the filtering parameter is dictated by the desired accuracy for the target scale. The second version involves control data in the target scale. This means that the optimal value for the filtering parameter is the value for which the difference between input and control data is the smallest. Analysis showed that the SG filter yielded the best results in general. The proposed filters can be considered as a new solution for automated cartographic line simplification.

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“…A polygon containing the resulting WS was generated in CloudCompare v2.6.1 (GPL Software) using a varied range of tools available within the software (i.e., "point list picking", "fit plane", "cross-section" and "extract cloud sections along polylines"). The central line was automatically determined using an in-house Python script that was based on the methodology proposed in [39,40] and a combination of suitable line simplification (i.e., Ramer-Douglas-Pencker [41]) and line smoothing (i.e., iterative averaging [42]) algorithms. The Douglas-Peucker method [41] aims to simplify line segments via iterative point removal.…”

Section: Hydromorphological Measure Estimationmentioning

confidence: 99%

“…The points with distances below the threshold are then removed. The iterative averaging algorithm [42] smooths the resulting simplified line. First, a triangle is defined from three points of the line segment.…”

Section: Hydromorphological Measure Estimationmentioning

confidence: 99%

In-Channel 3D Models of Riverine Environments for Hydromorphological Characterization

et al. 2018

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Recent legislative approaches to improve the quality of rivers have resulted in the design and implementation of extensive and intensive monitoring programmes that are costly and time consuming. An important component of assessing the ecological status of a water body as required by the Water Framework Directive is characterising the hydromorphology. Recent advances in autonomous operation and the spatial coverage of monitoring systems enables more rapid 3D models of the river environment to be produced. This study presents a Structure from Motion (SfM) semi-autonomous based framework for the estimation of key reach hydromorphological measures such as water surface area, wetted water width, bank height, bank slope and bank-full width, using in-channel stereo-imagery. The framework relies on a stereo-camera that could be positioned on an autonomous boat. The proposed approach is demonstrated along three 40 m long reaches with differing hydromorphological characteristics. Results indicated that optimal stereo-camera settings need to be selected based on the river appearance. Results also indicated that the characteristics of the reach have an impact on the estimation of the hydromorphological measures; densely vegetated banks, presence of debris and sinuosity along the reach increased the overall error in hydromorphological measure estimation. The results obtained highlight a potential way forward towards the autonomous monitoring of freshwater ecosystems.

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Smoothing Via Iterative Averaging (SIA)A Basic Technique for Line Smoothing

Cited by 11 publications

References 9 publications

Shaping pre-modern digital terrain models: The former topography at Charlemagne’s canal construction site

Shaping pre-modern digital terrain models: The former topography at Charlemagne’s canal construction site

Preliminary Analysis of Quality of Contour Lines Using Smoothing Algorithms

In-Channel 3D Models of Riverine Environments for Hydromorphological Characterization

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