Gully detection with Inverse Morphological Reconstruction Algorithm [data] (doi:10.11588/data/PXDR4M)

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Part 2: Study Description
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Document Description

Citation

Title:

Gully detection with Inverse Morphological Reconstruction Algorithm [data]

Identification Number:

doi:10.11588/data/PXDR4M

Distributor:

heiDATA

Date of Distribution:

2023-12-13

Version:

1

Bibliographic Citation:

Vallejo Orti, Miguel; Negussie, Kaleb; Corral, Eva; Höfle, Bernhard; Bubenzer, Olaf, 2023, "Gully detection with Inverse Morphological Reconstruction Algorithm [data]", https://doi.org/10.11588/data/PXDR4M, heiDATA, V1

Study Description

Citation

Title:

Gully detection with Inverse Morphological Reconstruction Algorithm [data]

Identification Number:

doi:10.11588/data/PXDR4M

Authoring Entity:

Vallejo Orti, Miguel (Heidelberg University, Institute of Geography)

Negussie, Kaleb (Namibia University of Science and Technology)

Corral, Eva (University of Huelva)

Höfle, Bernhard ((3D Geospatial Data Processing Group, Institute of Geography, Heidelberg University, Germany))

Bubenzer, Olaf ((Institute of Geography, Heidelberg University, Germany))

Distributor:

heiDATA

Access Authority:

Vallejo Orti, Miguel

Holdings Information:

https://doi.org/10.11588/data/PXDR4M

Study Scope

Keywords:

Earth and Environmental Sciences

Topic Classification:

gully erosion, digital elevation models, automatic detection

Abstract:

Characterization of micro-terrain features has been explored to detect gully objects in the terrain. An adaptation to the morphological reconstruction operator is implemented to detect gullies instead of buildings or other man-made structures. This operator can be configured to different gully depths and widths. The algorithm is based on successive geodesic dilations applied on a moving kernel. The geodesic dilation uses a mask (shifted copy of the original terrain) to generate a reconstructed surface, which ultimately can be subtracted from the original terrain to produce off-terrain elements or gully zones. Thus, the algorithm uses as inputs the original DEM, and a predefined height (mask shift) and width (kernel size) in meters, to customize the minimum detectable gully by the operator.

Methodology and Processing

Sources Statement

Data Access

Other Study Description Materials

Related Publications

Citation

Title:

Vallejo Orti, M.; Negussie, K.; Corral-Pazos-de-Provens, E.; Höfle, B.; Bubenzer, O. Comparison of Three Algorithms for the Evaluation of TanDEM-X Data for Gully Detection in Krumhuk Farm (Namibia). Remote Sens. 2019, 11, 1327. doi: 10.3390/rs11111327

Identification Number:

10.3390/rs11111327

Bibliographic Citation:

Vallejo Orti, M.; Negussie, K.; Corral-Pazos-de-Provens, E.; Höfle, B.; Bubenzer, O. Comparison of Three Algorithms for the Evaluation of TanDEM-X Data for Gully Detection in Krumhuk Farm (Namibia). Remote Sens. 2019, 11, 1327. doi: 10.3390/rs11111327

Other Study-Related Materials

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IMR_report.txt

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Example of Output data

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Other Study-Related Materials

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inverse_morphological_reconstruction.py

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Code

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Other Study-Related Materials

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MetaData_inverse_morphological_reconstuction.txt

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Code Metadata

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Other Study-Related Materials

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points_dem3.txt

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Example of Input data

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text/plain