Study of Digital Elevation Model (DEM) Extraction using Stereo Radargrammetry TerraSAR-X in Madiun Area – Elevation Accuracy Improvement

Sari, Inggit Lolita; Maulana, Rachmat; Dyatmika, Haris Suka; Suprijanto, Agus; Arief, Rahmat; Ali, Shadiq

doi:10.1051/e3sconf/20199404003

Cited by 4 publications

(2 citation statements)

References 4 publications

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“…As a result, several investigations on DEM have been conducted by researchers leading to many methodical propositions to reduce errors and increase accuracy. For example, Yue et al (2007), Cook et al (2012), Wilson (2012), Sari et al (2019), Hugenholtz et al (2013), Chen and Yue (2010), Warriner (2005), and Zhou et al (2012) suggested different methods for generating improved DEM. Further, DEM accuracy was evaluated by Hawker et al (2019) over Croatia.…”

Section: Background and Related Workmentioning

confidence: 99%

A Proposed Merging Methods of Digital Elevation Model Based on Artificial Neural Network and Interpolation Techniques for Improved Accuracy

Alemam,

YONG,

Sani-Mohammed

2023

Artificial Satellites

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The digital elevation model (DEM) is one of the most critical sources of terrain elevations, which are essential in various geoscience applications. Most of these applications need precise elevations, which are available at a high cost. Thus, sources like the Shuttle Radar Topography Mission (SRTM) DEM are frequently accessible to all users but with low accuracy. Consequently, many studies have tried to improve the accuracy of DEMs acquired from these free sources. Importantly, using the SRTM DEM is not recommended for an area that partly contains high-accuracy data. Thus, there is a need for a merging technique to produce a merged DEM of the whole area with improved accuracy. In recent years, advancements in geographic information systems (GIS) have improved data analysis by providing tools for applying merging techniques (like the minimum, maximum, last, first, mean, and blend (conventional methods)) to improve DEMs. In this article, DEM merging methods based on artificial neural network (ANN) and interpolation techniques are proposed. The methods are compared with other existing methods in commercial GIS software. The kriging, inverse distance weighted (IDW), and spline interpolation methods were considered for this investigation. The essential step for achieving the merging stage is the correction surface generation, which is used for modifying the SRTM DEM. Moreover, two cases were taken into consideration, i.e., the zeros border and the H border. The findings show that the proposed DEM merging methods (PDMMs) improved the accuracy of the SRTM DEM more than the conventional methods (CDMMs). The findings further show that the PDMMs of the H border achieved higher accuracy than the PDMMs of the zeros border, while kriging outperformed the other interpolation methods in both cases. The ANN outperformed all methods with the highest accuracy. Its improvements in the zeros and H border respectively reached 22.38% and 75.73% in elevation, 34.67% and 54.83% in the slope, and 40.28% and 52.22% in the aspect. Therefore, this approach would be cost-effective, especially in critical engineering projects.

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Section: Background and Related Workmentioning

confidence: 99%

A Proposed Merging Methods of Digital Elevation Model Based on Artificial Neural Network and Interpolation Techniques for Improved Accuracy

Alemam,

YONG,

Sani-Mohammed

2023

Artificial Satellites

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“…In this paper, a new type of neural network that directly processes point clouds is used. It respects the invariance of the arrangement of input points and divides the tree point clouds [37].…”

Section: Point Cloud Segmentation Algorithmmentioning

confidence: 99%

Research on a Single-Tree Point Cloud Segmentation Method Based on UAV Tilt Photography and Deep Learning Algorithm

2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Developing a robust point cloud segmentation algorithm for individual trees from an amount of point cloud data has great significance for tracking tree changes. This method can measure the size, growth and mortality of individual trees to track and understand forest carbon storage and variation. Traditional measurement methods are not only slow but tardy. In order to obtain forest information better and faster, this article focuses on two aspects: The first is using UAVs to obtain multi-view remote sensing images of the forest, then using the SFM algorithm to construct the forest sparse point cloud and PMVS algorithm to construct the dense point cloud. The second is that a targeted point cloud deep learning method is proposed to extract the point cloud of a single tree. The research results show that the accuracy of single-tree point cloud segmentation of deep learning methods is more than 90%, and the accuracy is far better than traditional planar image segmentation and point cloud segmentation. The combination of point cloud data acquisition with UAV remote sensing and point cloud deep learning algorithms can meet the needs of forestry surveys. It is undeniable that this method, as a forestry survey tool, has a large space for promotion and possible future development.

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Quality assessment and validation of TerraSAR-X digital surface model using LiDAR for urban areas in Jakarta

Arief

Suhermanto

Ali

et al. 2020

J. Phys.: Conf. Ser.

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Study of Digital Elevation Model (DEM) Extraction using Stereo Radargrammetry TerraSAR-X in Madiun Area – Elevation Accuracy Improvement

Cited by 4 publications

References 4 publications

A Proposed Merging Methods of Digital Elevation Model Based on Artificial Neural Network and Interpolation Techniques for Improved Accuracy

A Proposed Merging Methods of Digital Elevation Model Based on Artificial Neural Network and Interpolation Techniques for Improved Accuracy

Research on a Single-Tree Point Cloud Segmentation Method Based on UAV Tilt Photography and Deep Learning Algorithm

Quality assessment and validation of TerraSAR-X digital surface model using LiDAR for urban areas in Jakarta

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