A robust interpolation method for constructing digital elevation models from remote sensing data

Chen, Chuanfa; Liu, Fengying; Li, Yanyan; Yan, Changqing; Liu, Guolin

doi:10.1016/j.geomorph.2016.06.025

Cited by 33 publications

(19 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Good data quality is the guarantee of good TPPD performance. In this research, the linear interpolation method [21] is used to complete the missing traffic data, and the average smoothing method [22] is introduced to eliminate the noise data. With these two steps, the traffic data quality is improved greatly.…”

Section: Data Preprocessingmentioning

confidence: 99%

Traffic Peak Period Detection from an Image Processing View

Xiao

Wang

et al. 2018

Journal of Advanced Transportation

View full text Add to dashboard Cite

Traffic peak period detection is very important for the guidance and control of traffic flow. Most common methods for traffic peak period detection are based on data analysis. They have achieved good performance. However, the detection processes are not intuitional enough. Besides that, the accuracy of these methods needs to be improved further. From an image processing view, we introduce a concept in corner detection, sharpness, to detect the traffic peak periods in this paper. The proposed method takes the traffic peak period detection problem as a salient point detection problem and uses the image processing strategies to solve this problem. Firstly, it generates a speed curve image with the speed data. With this image, the method for detection of salient points is adopted to obtain the peak point candidates. If one candidate has the lowest speed value, this candidate is the peak point. Finally, the peak period is gotten by moving forward and backward the corresponding time of the peak point with a time interval. Experimental results show that the proposed method has achieved higher accuracy. More importantly, as the proposed method solves the traffic peak period detection problem from an image processing view, it has more intuition.

show abstract

Section: Data Preprocessingmentioning

confidence: 99%

Traffic Peak Period Detection from an Image Processing View

Xiao

Wang

et al. 2018

Journal of Advanced Transportation

View full text Add to dashboard Cite

show abstract

“…Research on surface-model precision has been ongoing, but it has mostly focused on the numerical precision of the elevation in the surface model [19,28,30,[35][36][37][38], and a few studies have considered shape reliability [31,36,54]. From the perspective of morphological characteristics, the numerical precision of elevation is only a measure of the elevation dispersion degree of points.…”

Section: Potential and Shortcomings Of The Morphological Precision Inmentioning

confidence: 99%

“…The data sources of morphological reconstructions are discrete and temporally and spatially limited, so appropriate reconstruction approaches are required in order to establish the continuous morphological surface model [18]. Since the 1970s, many scholars have proposed a series of interpolation methods for surface-model reconstruction, including polynomial interpolation, radial basis function interpolation, kriging interpolation, weighted average interpolation, triangle subdivision interpolation, and mathematical morphology interpolation, which have been widely used in many fields [19][20][21][22][23][24][25][26][27]. To evaluate the performance of reconstruction approaches for morphological surface models, many scholars have analyzed their respective advantages and disadvantages and their adaptability to different geomorphic types [28][29][30][31][32][33].…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, the theory and technique of the morphological precision assessment still have some shortcomings. In many studies, the morphological precision assessment is limited to the precision of the elevation, and the root mean square error (RMSE) and mean error (ME) are always used as the main indices to measure the morphological precision [19,28,30,[35][36][37][38]. However, Desmet and Govers defined the morphological precision as a compromise between numerical precision and shape reliability [39].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Morphological Precision Assessment of Reconstructed Surface Models for a Coral Atoll Lagoon

Wang

Zhang

et al. 2018

Sustainability

View full text Add to dashboard Cite

In addition to remote-sensing monitoring, reconstructing morphologic surface models through interpolation is an effective means to reflect the geomorphological evolution, especially for the lagoons of coral atolls, which are underwater. However, which interpolation method is optimal for lagoon geomorphological reconstruction and how to assess the morphological precision have been unclear. To address the aforementioned problems, this study proposed a morphological precision index system including the root mean square error (RMSE) of the elevation, the change rate of the local slope shape (CRLSS), and the change rate of the local slope aspect (CRLSA), and introduced the spatial appraisal and valuation approach of environment and ecosystems (SAVEE). In detail, ordinary kriging (OK), inverse distance weighting (IDW), radial basis function (RBF), and local polynomial interpolation (LPI) were used to reconstruct the lagoon surface models of a typical coral atoll in South China Sea and the morphological precision of them were assessed, respectively. The results are as follows: (i) OK, IDW, and RBF exhibit the best performance in terms of RMSE (0.3584 m), CRLSS (51.43%), and CRLSA (43.29%), respectively, while with insufficiently robust when considering all three aspects; (ii) IDW, LPI, and RBF are suitable for lagoon slopes, lagoon bottoms, and patch reefs, respectively; (iii) The geomorphic decomposition scale is an important factor that affects the precision of geomorphologic reconstructions; and, (iv) This system and evaluation approach can more comprehensively consider the differences in multiple precision indices.

show abstract

“…However, according to Aguilar (2005), among all traditional interpolation methods, such as natural neighbour (NN), ANUDEM, ordinary Kriging (OK), and the multiquadric method (MQ), MQ is best when it is used for remote sensing data such as LIDAR. Chen (2016) put forward a MQ-M method that is more adaptable to outliers in remote sensing data on the basis of the MQ method [26]. Different DEMs are constructed by diverse interpolation methods and with different parameters or different numbers of local sampling points for estimating the elevation of points to be interpolated.…”

mentioning

confidence: 99%

Effect of DEM Interpolation Neighbourhood on Terrain Factors

Zhu

Liu

Jing

et al. 2019

IJGI

View full text Add to dashboard Cite

Topographic factors such as slope and aspect are essential parameters in depicting the structure and morphology of a terrain surface. We study the effect of the number of points in the neighbourhood of a digital elevation model (DEM) interpolation method on mean slope, mean aspect, and RMSEs of slope and aspect from the interpolated DEM. As the moving least squares (MLS) method can maintain the inherent properties and other characteristics of a surface, this method is chosen for DEM interpolation. Three areas containing different types of topographic features are selected for study. Simulated data from a Gauss surface is also used for comparison. First, the impact of the number of points on the DEM root mean square error (RMSE) is analysed. The DEM RMSE in the three study areas decreases gradually with the number of points in the neighbourhood. In addition, the effect of the number of points in the neighbourhood on mean slope and mean aspect was studied across varying topographies through regression analysis. The two variables respond differently to changes in terrain. However, the RMSEs of the slope and aspect in all study areas are logarithmically related to the number of points in the neighbourhood and the values decrease uniformly as the number of points in the neighbourhood increases. With more points in the neighbourhood, the RMSEs of the slope and aspect are not sensitive to topography differences and the same trends are observed for the three studied quantities. Results for the Gauss surface are similar. Finally, this study analyses the spatial distribution of slope and aspect errors. The slope error is concentrated in ridges, valleys, steep-slope areas, and ditch edges while the aspect error is concentrated in ridges, valleys, and flat regions. With more points in the neighbourhood, the number of grid cells in which the slope error is greater than 15° is gradually reduced. With similar terrain types and data sources, if the calculation efficiency is not a concern, sufficient points in the spatial autocorrelation range should be analysed in the neighbourhood to maximize the accuracy of the slope and aspect. However, selecting between 10 and 12 points in the neighbourhood is economical.

show abstract

A robust interpolation method for constructing digital elevation models from remote sensing data

Cited by 33 publications

References 44 publications

Traffic Peak Period Detection from an Image Processing View

Traffic Peak Period Detection from an Image Processing View

Morphological Precision Assessment of Reconstructed Surface Models for a Coral Atoll Lagoon

Effect of DEM Interpolation Neighbourhood on Terrain Factors

Contact Info

Product

Resources

About