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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B3, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
Figure 2. Flowchart of the proposed method
1. Recognize primitives and measure initial
parameters. With the help of optical imagery and
LiDAR point cloud, the building is decomposed into
several primitives. Then the primitive’s parameters
are measured roughly on LiDAR point cloud and
optical imagery, such as length, width, height,
orientation and translation of the primitive. These
measurements can be used as fixed values
(constraints) or initial values in the following
optimization procedure.
2. Extract features. Corners are detected/selected on
the optical imagery, and planes are detected/selected
in the LiDAR point cloud. These features will be
used as observed values/observations in the following
optimization procedure.
3. Compute features. Based on the type and
parameters of primitives, the 3D coordinates of the
primitives’ features, such as corners, can be
calculated. They will be used as model/computed
values in the following optimization procedure.
4. Optimize parameters. When a 3D building model
has correct shape and is located in the correct place
in 3D space, two conditions will be satisfied. Firstly,
the back-projections of primitive's vertexes
(computed features) on the optical image should
perfectly superpose on the measured corners
(extracted features). Secondly, the primitive's
vertexes should be exactly on the planes which are
formed by LiDAR point cloud. These two conditions
can be expressed respectively by Collinearity
Equation and 3D Plane Equation, and then a cost
function can be established using these two
mathematical models. The inputs of this cost function
are observed values, model values, and initial values
above. When the optimization procedure is finished,
the optimized/refined primitives’ parameters will be
outputted.
Finally, 3D building can be represented by these primitives
with the optimized parameters.
The proposed method was applied to a dataset of an ISPRS test
project. The organizer of this test project evaluated the
submitted reconstructed 3D model using reference data. In the
next section, first is the description of test data, followed by
the introduction of data processing, finally evaluating result is
analyzed and discussed.
3. EXPERIMENTAL RESULT AND DISCUSSION
3.1 Description of Data Set
The test data set was captured over Vaihingen in Germany.
The data set is a subset of the data used for the test of digital
aerial cameras carried out by the German Association of
Photogrammetry and Remote Sensing (DGPF) (Cramer, 2010).
The ground resolution of the digital aerial images is 8 cm. The
Vaihingen test data set provided by DGPF also contains
Airborne Laserscanner (ALS) data. The entire DGPF data set
consists of 10 ALS strips. Inside an individual strip the average
point density is 4 pts/m? (Haala et al., 2010).
The test data consists of three test areas for which reference
data for various object classes are available (Spreckels et al.,
2010). In this paper, Area 3 “Residential Area” was selected; it
is a purely residential area with small detached houses. Most
of buildings in this area can be represented by hip-roof
primitive. Fig. 3 shows the digital image of this test area.
Figure 3. Digital image of the test area
3.2 Task and Data Processing
This ISPRS Test Project has two tasks, Urban Classification
and 3D Building Reconstruction. The task of this paper is the
latter. The goal of this task is to derive a complete, correct, and
accurate segmentation of the roof planes in the provided data.
The detailed 3D models of the building roofs in the test areas
should be generated. The level of detail should correspond to
LoD2 of the CityGML standard.
The workflow of Fig. 2 was applied to the test data to generate
3D building models. It should be noted, at current stage, some
works were done in interactive mode. Both building's corners
in images and building's planes in point cloud were manually
extracted.
3.3 Experimental Result
After data processing, 3D building models were reconstructed.
The requirement of submitted result of ISPRS Test Project is