CMRT09

Stilla, Uwe

Bibliographic data

Monograph

Persistent identifier:: 856955019

Author:: Stilla, Uwe

Title:: CMRT09

Sub title:: object extraction for 3D city models, road databases, and traffic monitoring ; concepts, algorithms and evaluation ; Paris, France, September 3 - 4, 2009 ; [joint conference of ISPRS working groups III/4 and III/5]

Scope:: X, 234 Seiten

Year of publication:: 2009

Place of publication:: Lemmer

Publisher of the original:: GITC

Identifier (digital):: 856955019

Illustration:: Illustrationen, Diagramme, Karten

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Monograph

Collection:: Earth sciences

Chapter

Title:: DETECTION OF BUILDINGS AT AIRPORT SITES USING IMAGES & LIDAR DATA AND A COMBINATION OF VARIOUS METHODS Demir, N., Poli, D., Baltsavias, E.

Document type:: Monograph

Structure type:: Chapter

In: Stilla U, Rottensteiner F, Paparoditis N (Eds) CMRT09. IAPRS, Vol. XXXVIII, Part 3/W4 — Paris, France, 3-4 September, 2009 vegetation detection). In addition, new synthetic bands were generated from the selected channels: a) 3 images from principal component analysis (PCI, PC2, PC3); b) one image from NDVI computation using the NIR-R channels and c) one saturation image (S) obtained by converting the NIR-R-G channels in the IHS (Intensity, Hue, Saturation) colour space. The separability of the target classes was analyzed through use of plots by mean and standard deviation for each class and channel and divergence matrix analysis of all possible combinations of the three CIR channels and the additional channels, mentioned above. The analysis showed that: • G and PC2 have high correlation with other bands • NIR-R-PC1 is the best combination based on the plot analysis • NIR band shows good separability based on the divergence analysis • PC1-NDVI-S combination shows best separability over three-band combinations based on the divergence analysis. Therefore, the combination NIR-R-PC1-NDVI -S was selected for classification. The maximum likelihood classification method was used. As expected from their low values in the divergence matrix, grass and trees, airport buildings and residential houses, airport corridors and bare ground, airport buildings and bare ground could not be separated. Using the height information from nDSM, airport ground and bare ground and roads were fused into “ground” and airport buildings with residential houses into “buildings”, while trees and grass, as well as buildings and ground could be separated. The final classification is shown in Figure 2. 84% of the building class is correctly classified, while All of 109 buildings have been detected but not fully, the omission error is 9% . Aircrafts and vehicles are again mixed with buildings. mr. ss ymwn Figure 2. Building detection result from method 2. (Left: airport buildings, Right: residential area). 4.3 Building detection using density of raw Lidar DTM and NDVI (Method 3) Buildings and other objects, like high or dense trees, vehicles, aircrafts, etc. are characterized by null or very low density in the DTM point cloud. Using the vegetation class from NDVI channel as a mask, the areas covered by trees are eliminated, while small objects (aircrafts, vehicles) are eliminated by deleting them, if their area is smaller than 25m 2 . Thus, only buildings remain (Figure 3). 85% of building class pixels are correctly classified, while 108 of 109 buildings have been detected but not fully extracted, the omission error is 8% . Figure 3. Building detection result from method 3. (Left: airport buildings. Right: residential area). 4.4 Building and tree detection from Lidar data (Method 4) As mentioned above, in the raw DSM data the point density is generally much higher at trees than at open terrain or buildings. On the other hand, tree areas have low horizontal point density in the raw DTM data. We start from regions that are voids or have low density in the raw DTM (see Method 3). These regions represent mainly buildings and trees and are used as mask to select the raw DSM points for further analysis. In the next step, we used a search window over the raw Lidar DSM data with a size of 5 m x 5 m. Neighboring windows have an overlap of 50%. The window size has a relation with the number of points in the window and the number of the points in the search window affects the quality of the detection result. The method uses all points in the window and labels them as tree if all parameters below have been met. The size of 25m 2 has been agreed to be enough to extract one single tree. A bigger size may result in wrong detection especially in areas where the buildings are neighboring with single trees. On the other hand, the data has low point density: 1 pt / 2 m 2 , that means about 13 pts / 25 nr. A smaller size will contain less points and this may not be enough for the detection. The points in each search window are projected onto the xz and yz planes and divided for each projection in eight equal sub- regions using x m j n , x m i d , x max , z mm z m j d ] z m j d 2 2 m j(j3 z max as boundary values of sub-regions, with x mid = x min + 2.5m , x max — x m id "F 2.5m, z m j d ]— z rn i n +(z max -z m j n )/4, z ivnd 2 ~z n - nn +2 (z max - Zmin)/4, z mid3 =z min +3*(z max -z min )/4 and similarly for the yz projection. The density in the eight sub-regions is computed. The first step is the detection of trees and the second the subtraction of tree points from all off-terrain points. The trees have been extracted by four different parameters. The parameters have been calculated using tree-masked areas of the raw Lidar DSM data. The tree mask has been generated by Method 2. Then, the calculated parameters (the average of all search windows) have been applied to the raw Lidar DSM data for detection of trees. The first parameter (s) is similarity of surface normal vectors. We assume that the tree points would not fit to a plane. With selection of three random points in the search window, the surface normal vectors have been calculated n (number of points in search window) times. Then, all calculated vectors have been compared among each other. In case of similar value of compared vectors, the similarity value was increased by adding 1. In the tree masked points, the parameter (s) has been calculated as smaller than 2. The second parameter (vd) is the number of the eight sub-regions which contain at least one point. The trees have high Lidar point density vertically. Thus, at trees more sub-regions contain Lidar points. Using the tree mask, we have observed that at least 5 out of the 8 sub-regions contain points. Thus, the parameter (vd) has been selected as

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