Milenković, Milutin

Link to this page

Authority KeyName Variants
a7448b39-7a9a-48cd-bb9f-94761438c94b
  • Milenković, Milutin (3)
Projects

Author's Bibliography

Refinement of Individual Tree Detection Results Obtained from Airborne Laser Scanning Data for a Mixed Natural Forest

Brodić, Nenad; Cvijetinović, Željko; Milenković, Milutin; Kovačević, Jovan; Stančić, Nikola; Mitrović, Momir; Mihajlović, Dragan

(MDPI, 2022)

TY  - JOUR
AU  - Brodić, Nenad
AU  - Cvijetinović, Željko
AU  - Milenković, Milutin
AU  - Kovačević, Jovan
AU  - Stančić, Nikola
AU  - Mitrović, Momir
AU  - Mihajlović, Dragan
PY  - 2022
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/2775
AB  - Numerous semi- and fully-automatic algorithms have been developed for individual tree detection from airborne laser-scanning data, but different rates of falsely detected treetops also accompany their results. In this paper, we proposed an approach that includes a machine learning based refinement step to reduce the number of falsely detected treetops. The approach involves the local maxima filtering and segmentation of the canopy height model to extract different segment level features used for the classification of treetop candidates. The study was conducted in a mixed temperate forest, predominantly deciduous, with a complex topography and an area size of 0.6 km × 4 km. The classification model’s training was performed by five machine learning approaches: Random Forest (RF), Extreme Gradient Boosting, Artificial Neural Network, the Support Vector
Machine, and Logistic Regression. The final classification model with optimal hyperparameters was adopted based on the best-performing classifier (RF). The overall accuracy (OA) and kappa coefficient (κ) obtained from the ten-fold cross validation for the training data were 90.4% and 0.808, respectively. The prediction of the test data resulted in an OA = 89.0% and a κ = 0.757. This indicates that the proposed method could be an adequate solution for the reduction of falsely detected treetops before tree crown segmentation, especially in deciduous forests.
PB  - MDPI
T2  - Remote Sensing, 2022, 14(21), 5345
T1  - Refinement of Individual Tree Detection Results Obtained from Airborne Laser Scanning Data for a Mixed Natural Forest
VL  - 14
DO  - 10.3390/rs14215345
ER  - 
@article{
author = "Brodić, Nenad and Cvijetinović, Željko and Milenković, Milutin and Kovačević, Jovan and Stančić, Nikola and Mitrović, Momir and Mihajlović, Dragan",
year = "2022",
abstract = "Numerous semi- and fully-automatic algorithms have been developed for individual tree detection from airborne laser-scanning data, but different rates of falsely detected treetops also accompany their results. In this paper, we proposed an approach that includes a machine learning based refinement step to reduce the number of falsely detected treetops. The approach involves the local maxima filtering and segmentation of the canopy height model to extract different segment level features used for the classification of treetop candidates. The study was conducted in a mixed temperate forest, predominantly deciduous, with a complex topography and an area size of 0.6 km × 4 km. The classification model’s training was performed by five machine learning approaches: Random Forest (RF), Extreme Gradient Boosting, Artificial Neural Network, the Support Vector
Machine, and Logistic Regression. The final classification model with optimal hyperparameters was adopted based on the best-performing classifier (RF). The overall accuracy (OA) and kappa coefficient (κ) obtained from the ten-fold cross validation for the training data were 90.4% and 0.808, respectively. The prediction of the test data resulted in an OA = 89.0% and a κ = 0.757. This indicates that the proposed method could be an adequate solution for the reduction of falsely detected treetops before tree crown segmentation, especially in deciduous forests.",
publisher = "MDPI",
journal = "Remote Sensing, 2022, 14(21), 5345",
title = "Refinement of Individual Tree Detection Results Obtained from Airborne Laser Scanning Data for a Mixed Natural Forest",
volume = "14",
doi = "10.3390/rs14215345"
}
Brodić, N., Cvijetinović, Ž., Milenković, M., Kovačević, J., Stančić, N., Mitrović, M.,& Mihajlović, D.. (2022). Refinement of Individual Tree Detection Results Obtained from Airborne Laser Scanning Data for a Mixed Natural Forest. in Remote Sensing, 2022, 14(21), 5345
MDPI., 14.
https://doi.org/10.3390/rs14215345
Brodić N, Cvijetinović Ž, Milenković M, Kovačević J, Stančić N, Mitrović M, Mihajlović D. Refinement of Individual Tree Detection Results Obtained from Airborne Laser Scanning Data for a Mixed Natural Forest. in Remote Sensing, 2022, 14(21), 5345. 2022;14.
doi:10.3390/rs14215345 .
Brodić, Nenad, Cvijetinović, Željko, Milenković, Milutin, Kovačević, Jovan, Stančić, Nikola, Mitrović, Momir, Mihajlović, Dragan, "Refinement of Individual Tree Detection Results Obtained from Airborne Laser Scanning Data for a Mixed Natural Forest" in Remote Sensing, 2022, 14(21), 5345, 14 (2022),
https://doi.org/10.3390/rs14215345 . .
1

Airborne Laser Scanning (ALS) point cloud ground filtering for area of an active landslide (Doren, Western Austria)

Brodić, Nenad; Cvijetinović, Željko; Milenković, Milutin; Dorninger, Peter; Mitrović, Momir

(2014)

TY  - CONF
AU  - Brodić, Nenad
AU  - Cvijetinović, Željko
AU  - Milenković, Milutin
AU  - Dorninger, Peter
AU  - Mitrović, Momir
PY  - 2014
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/1176
AB  - Ground filtering of point cloud is the primary step required for Digital Terrain Model (DTM) generation. The procedure is especially interesting for forested areas, since LiDAR systems can measure terrain elevation under vegetation cover with a high level of penetration. This work analyzes the potential of ALS data ground filtering for area of an active landslide. The results of ALS filtering, for example, may improve geomorphological and motiondetection studies. ALS data was collected during flight campaign 2011 under leaf-off conditions for Doren region, Vorarlberg, Western Austria. In this area, non-ground objects are mostly low vegetation such as shrubs, small trees etc. The vegetation is more dense in lower part of the landslide where erosion is smaller. Vegetation points can be removed based on the hypothesis that these are significantly higher than their neighboring points. However, in case of steep terrain, ground points may have the same heights as vegetation points, and thus, local slope should be considered. Also, if terrain roughness increases, the classification may become even more complex. Software system OPALS (Orientation and Processing of Airborne Laser Scanning data, Vienna University of Technology) was used for processing the ALS data. Labeling ground points has been made using physical and geometrical attributes (parameters) of ALS points. Also additional attributes were calculated in order to improve extraction. Since bare ground surface is usually smooth and continuous unlike vegetation, standard deviation of local elevations was used as roughness measure to differentiate these surfaces. EchoRatio (ER) was adopted as a measure of surface penetrability, while number of echoes and differentiation between echoes (EchoNumber) were also deployed in filtering. Since the ground points are measurements from bare-earth that are usually the lowest surface features in a local area, normalized height was defined as a rank of neighboring points. Additionally, a so-called openness parameter was used as a convexity/concavity measure of surface. All of the mentioned point attributes have been combined in a decision tree in order to extract bare ground points. Based on a preliminary analysis, it is noted that implemented filtering procedure has difficulties with surfaces with rough terrain or steep relief. There is an assumption for lack of ground points in areas with dense vegetation that reduced the penetration of laser beams to the ground (lower part of a landslide). Also, points representing low vegetation were often labeled as ground points. Procedure produced holes in point cloud which demanded appropriate interpolation methods to be applied.
C3  - EGU General Assembly 2014, held 27 April - 2 May, 2014 in Vienna, Austria
T1  - Airborne Laser Scanning (ALS) point cloud ground filtering for area of an active landslide (Doren, Western Austria)
EP  - 15341
VL  - 16
UR  - https://hdl.handle.net/21.15107/rcub_grafar_1176
ER  - 
@conference{
author = "Brodić, Nenad and Cvijetinović, Željko and Milenković, Milutin and Dorninger, Peter and Mitrović, Momir",
year = "2014",
abstract = "Ground filtering of point cloud is the primary step required for Digital Terrain Model (DTM) generation. The procedure is especially interesting for forested areas, since LiDAR systems can measure terrain elevation under vegetation cover with a high level of penetration. This work analyzes the potential of ALS data ground filtering for area of an active landslide. The results of ALS filtering, for example, may improve geomorphological and motiondetection studies. ALS data was collected during flight campaign 2011 under leaf-off conditions for Doren region, Vorarlberg, Western Austria. In this area, non-ground objects are mostly low vegetation such as shrubs, small trees etc. The vegetation is more dense in lower part of the landslide where erosion is smaller. Vegetation points can be removed based on the hypothesis that these are significantly higher than their neighboring points. However, in case of steep terrain, ground points may have the same heights as vegetation points, and thus, local slope should be considered. Also, if terrain roughness increases, the classification may become even more complex. Software system OPALS (Orientation and Processing of Airborne Laser Scanning data, Vienna University of Technology) was used for processing the ALS data. Labeling ground points has been made using physical and geometrical attributes (parameters) of ALS points. Also additional attributes were calculated in order to improve extraction. Since bare ground surface is usually smooth and continuous unlike vegetation, standard deviation of local elevations was used as roughness measure to differentiate these surfaces. EchoRatio (ER) was adopted as a measure of surface penetrability, while number of echoes and differentiation between echoes (EchoNumber) were also deployed in filtering. Since the ground points are measurements from bare-earth that are usually the lowest surface features in a local area, normalized height was defined as a rank of neighboring points. Additionally, a so-called openness parameter was used as a convexity/concavity measure of surface. All of the mentioned point attributes have been combined in a decision tree in order to extract bare ground points. Based on a preliminary analysis, it is noted that implemented filtering procedure has difficulties with surfaces with rough terrain or steep relief. There is an assumption for lack of ground points in areas with dense vegetation that reduced the penetration of laser beams to the ground (lower part of a landslide). Also, points representing low vegetation were often labeled as ground points. Procedure produced holes in point cloud which demanded appropriate interpolation methods to be applied.",
journal = "EGU General Assembly 2014, held 27 April - 2 May, 2014 in Vienna, Austria",
title = "Airborne Laser Scanning (ALS) point cloud ground filtering for area of an active landslide (Doren, Western Austria)",
pages = "15341",
volume = "16",
url = "https://hdl.handle.net/21.15107/rcub_grafar_1176"
}
Brodić, N., Cvijetinović, Ž., Milenković, M., Dorninger, P.,& Mitrović, M.. (2014). Airborne Laser Scanning (ALS) point cloud ground filtering for area of an active landslide (Doren, Western Austria). in EGU General Assembly 2014, held 27 April - 2 May, 2014 in Vienna, Austria, 16.
https://hdl.handle.net/21.15107/rcub_grafar_1176
Brodić N, Cvijetinović Ž, Milenković M, Dorninger P, Mitrović M. Airborne Laser Scanning (ALS) point cloud ground filtering for area of an active landslide (Doren, Western Austria). in EGU General Assembly 2014, held 27 April - 2 May, 2014 in Vienna, Austria. 2014;16:null-15341.
https://hdl.handle.net/21.15107/rcub_grafar_1176 .
Brodić, Nenad, Cvijetinović, Željko, Milenković, Milutin, Dorninger, Peter, Mitrović, Momir, "Airborne Laser Scanning (ALS) point cloud ground filtering for area of an active landslide (Doren, Western Austria)" in EGU General Assembly 2014, held 27 April - 2 May, 2014 in Vienna, Austria, 16 (2014),
https://hdl.handle.net/21.15107/rcub_grafar_1176 .

Shuttle radar topography mission: Availability of data and the accuracy achieved

Samardžić-Petrović, Mileva; Milenković, Milutin

(Srpsko geografsko društvo, Beograd, 2010)

TY  - JOUR
AU  - Samardžić-Petrović, Mileva
AU  - Milenković, Milutin
PY  - 2010
UR  - https://grafar.grf.bg.ac.rs/handle/123456789/315
AB  - This paper aim is to determine accuracy of digital terrain model (DTM) formed upon Shuttle Radar Topography Mission (SRTM) data, for the region of the Republic of Serbia. Main characteristics of SRTM will be introduced, along with short description of determining DTM based on SRTM data and accuracy of such derived digital terrain model.
AB  - Cilj ovog rada je da se odredi tačnost digitalnog modela terena (DMT) formiranog na osnovu podataka šatlove radarske topografske misije (Shuttle Radar Topography Mission - SRTM), za područje Republike. Srbije. U radu će biti predstavljene glavne karakteristike SRTM-a kao i kratak opis samog postupka određivanja DMT-a na osnovu SRTM podataka i određivanje tačnosti tako dobijenog digitalnog modela terena.
PB  - Srpsko geografsko društvo, Beograd
T2  - Glasnik Srpskog geografskog društva
T1  - Shuttle radar topography mission: Availability of data and the accuracy achieved
T1  - Shuttle radar topography mission - dostupnost podataka i ostvarena tačnost
EP  - 72
IS  - 1
SP  - 51
VL  - 90
DO  - 10.2298/GSGD1001051S
ER  - 
@article{
author = "Samardžić-Petrović, Mileva and Milenković, Milutin",
year = "2010",
abstract = "This paper aim is to determine accuracy of digital terrain model (DTM) formed upon Shuttle Radar Topography Mission (SRTM) data, for the region of the Republic of Serbia. Main characteristics of SRTM will be introduced, along with short description of determining DTM based on SRTM data and accuracy of such derived digital terrain model., Cilj ovog rada je da se odredi tačnost digitalnog modela terena (DMT) formiranog na osnovu podataka šatlove radarske topografske misije (Shuttle Radar Topography Mission - SRTM), za područje Republike. Srbije. U radu će biti predstavljene glavne karakteristike SRTM-a kao i kratak opis samog postupka određivanja DMT-a na osnovu SRTM podataka i određivanje tačnosti tako dobijenog digitalnog modela terena.",
publisher = "Srpsko geografsko društvo, Beograd",
journal = "Glasnik Srpskog geografskog društva",
title = "Shuttle radar topography mission: Availability of data and the accuracy achieved, Shuttle radar topography mission - dostupnost podataka i ostvarena tačnost",
pages = "72-51",
number = "1",
volume = "90",
doi = "10.2298/GSGD1001051S"
}
Samardžić-Petrović, M.,& Milenković, M.. (2010). Shuttle radar topography mission: Availability of data and the accuracy achieved. in Glasnik Srpskog geografskog društva
Srpsko geografsko društvo, Beograd., 90(1), 51-72.
https://doi.org/10.2298/GSGD1001051S
Samardžić-Petrović M, Milenković M. Shuttle radar topography mission: Availability of data and the accuracy achieved. in Glasnik Srpskog geografskog društva. 2010;90(1):51-72.
doi:10.2298/GSGD1001051S .
Samardžić-Petrović, Mileva, Milenković, Milutin, "Shuttle radar topography mission: Availability of data and the accuracy achieved" in Glasnik Srpskog geografskog društva, 90, no. 1 (2010):51-72,
https://doi.org/10.2298/GSGD1001051S . .
1