3 research outputs found
BUILDING CHANGE DETECTION FROM LIDAR POINT CLOUD DATA BASED ON CONNECTED COMPONENT ANALYSIS
Building data are one of the important data types in a topographic database. Building change detection after a period of time is necessary
for many applications, such as identification of informal settlements. Based on the detected changes, the database has to be updated
to ensure its usefulness. This paper proposes an improved building detection technique, which is a prerequisite for many building
change detection techniques. The improved technique examines the gap between neighbouring buildings in the building mask in order
to avoid under segmentation errors. Then, a new building change detection technique from LIDAR point cloud data is proposed.
Buildings which are totally new or demolished are directly added to the change detection output. However, for demolished or extended
building parts, a connected component analysis algorithm is applied and for each connected component its area, width and height are
estimated in order to ascertain if it can be considered as a demolished or new building part. Finally, a graphical user interface (GUI)
has been developed to update detected changes to the existing building map. Experimental results show that the improved building
detection technique can offer not only higher performance in terms of completeness and correctness, but also a lower number of undersegmentation
errors as compared to its original counterpart. The proposed change detection technique produces no omission errors and
thus it can be exploited for enhanced automated building information updating within a topographic database. Using the developed
GUI, the user can quickly examine each suggested change and indicate his/her decision with a minimum number of mouse clicks
Building change detection from LIDAR point cloud data based on connected component analysis
Building data are one of the important data types in a topographic database. Building change detection after a period of time is necessary for many applications, such as identification of informal settlements. Based on the detected changes, the database has to be updated to ensure its usefulness. This paper proposes an improved building detection technique, which is a prerequisite for many building change detection techniques. The improved technique examines the gap between neighbouring buildings in the building mask in order to avoid under segmentation errors. Then, a new building change detection technique from LIDAR point cloud data is proposed. Buildings which are totally new or demolished are directly added to the change detection output. However, for demolished or extended building parts, a connected component analysis algorithm is applied and for each connected component its area, width and height are estimated in order to ascertain if it can be considered as a demolished or new building part. Finally, a graphical user interface (GUI) has been developed to update detected changes to the existing building map. Experimental results show that the improved building detection technique can offer not only higher performance in terms of completeness and correctness, but also a lower number of under-segmentation errors as compared to its original counterpart. The proposed change detection technique produces no omission errors and thus it can be exploited for enhanced automated building information updating within a topographic database. Using the developed GUI, the user can quickly examine each suggested change and indicate his/her decision with a minimum number of mouse clicks
Building change detection from remotely sensed data using machine learning techniques
As remote sensing data plays an increasingly important role in many fields, many
countries have established geographic information systems. However, such systems
usually suffer from obsolete scene details, making the development of change detection
technology critical. Building changes are important in practice, as they are valuable in
urban planning and disaster rescue. This thesis focuses on building change detection
from remotely sensed data using machine learning techniques.
Supervised classification is a traditional method for pixel level change detection, and
relies on a suitable training dataset. Since different training datasets may affect the
learning performance differently, the effects of dataset characteristics on pixel level
building change detection are first studied. The research is conducted from two angles,
namely the imbalance and noise in the training dataset, and multiple correlations among
different features. The robustness of some supervised learning algorithms to unbalanced
and noisy training datasets is examined, and the results are interpreted from a theoretical
perspective. A solution for handling multiple correlations is introduced, and its
performance on and applicability to building change detection is investigated. Finally,
an object-based post processing technique is proposed using prior knowledge to further
suppress false alarms.
A novel corner based Markov random field (MRF) method is then proposed for
exploring spatial information and contextual relations in changed building outline
detection. Corners are treated as vertices in the graph, and a new method is proposed for
determining neighbourhood relations. Energy terms in the proposed method are
constructed using spatial features to describe building characteristics. An optimal
solution indicates spatial features belonging to changed buildings, and changed areas are
revealed based on novel linking processes.
Considering the individual advantages of pixel level, contextual and spatial features, an
MRF based combinational method is proposed that exploits spectral, spatial and
contextual features in building change detection. It consists of pixel level detection and
corner based refinement. Pixel level detection is first conducted, which provides an
initial indication of changed areas. Corner based refinement is then implemented to
further refine the detection results. Experimental results and quantitative analysis
demonstrate the capacity and effectiveness of the proposed methods