A Robust Gradient-based Building Area and Plane Extraction Method
thesis
posted on 2017-01-04, 22:02 authored by Fasahat Ullah SiddiquiExtracting
buildings from remote sensing data is an essential task in many applications.
Currently, automatic building extraction from complex scenes is still
challenging. This is due to the presence of dense vegetation in a site,
variability in building sizes, materials and structures of building roof. For
decades, researchers have continued to work on building extraction using two
different kinds of data, i.e photogrammetric images and Light Detection And
Ranging (LiDAR) data. The existing work has generally ignored the analysis of
building material properties and has limited their study to the extraction of
only those buildings with non-transparent roof. Thus existing methods are not
effective in extracting buildings with a transparent roof. With their
predefined size parameter settings, these methods might also fail in extracting
small buildings. Furthermore, they overlook the importance of a local feature
analysis in refining the building boundaries and eliminating the vegetation
from complex scenes. For extracting the building details, i.e. the fine
structures of roof planes, very limited attention has been paid to
fusing/integrating the extracted features from the photogrammetric images and
the LiDAR data. The novel methods we have proposed in this thesis aim to
address these limitations.
First, we have proposed a gradient-based building area extraction method which analyses the building properties, i.e. orientations and materials of buildings, from the photogrammetric images and the LiDAR data. This enables the proposed method to effectively extract buildings with a transparent roof. The proposed method also does not depend on the shape or size parameters as well; thereby it is more effective in extracting buildings of a larger size range. Next, we have proposed a building refinement process that utilises the local building feature analyses. It avoids many empirically set parameters such as height and shape thresholds. This enables the proposed refinement process to eliminate vegetation more effectively and to extract building portions
at a lower height. Finally, we take advantage of the gradient concept in our proposed gradient-based building extraction method to extract the boundaries of building planes which are refined by using a new plane boundary regularisation process based on integration of the features from both input data. As a result, better refined structures (i.e. a smooth boundary) of building roof planes are extracted. In addition, the proposed method can also extract small roof planes as size or shape thresholds are not used.
The performance of the proposed method is qualitatively and quantitatively evaluated on two different benchmark data sets. Our experimental results have shown that our proposed methods are effective in addressing the limitations described above and have outperformed existing state-of-the-art baseline methods.
First, we have proposed a gradient-based building area extraction method which analyses the building properties, i.e. orientations and materials of buildings, from the photogrammetric images and the LiDAR data. This enables the proposed method to effectively extract buildings with a transparent roof. The proposed method also does not depend on the shape or size parameters as well; thereby it is more effective in extracting buildings of a larger size range. Next, we have proposed a building refinement process that utilises the local building feature analyses. It avoids many empirically set parameters such as height and shape thresholds. This enables the proposed refinement process to eliminate vegetation more effectively and to extract building portions
at a lower height. Finally, we take advantage of the gradient concept in our proposed gradient-based building extraction method to extract the boundaries of building planes which are refined by using a new plane boundary regularisation process based on integration of the features from both input data. As a result, better refined structures (i.e. a smooth boundary) of building roof planes are extracted. In addition, the proposed method can also extract small roof planes as size or shape thresholds are not used.
The performance of the proposed method is qualitatively and quantitatively evaluated on two different benchmark data sets. Our experimental results have shown that our proposed methods are effective in addressing the limitations described above and have outperformed existing state-of-the-art baseline methods.
