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:: A TEST OF AUTOMATIC BUILDING CHANGE DETECTION APPROACHES Nicolas Champion, Franz Rottensteiner, Leena Matikainen, Xinlian Liang, Juha Hyyppä and Brian P. Olsen

Document type:: Monograph

Structure type:: Chapter

CMRT09: Object Extraction for 3D City Models, Road Databases and Traffic Monitoring - Concepts, Algorithms, and Evaluation Sample Distance (GSD) of all input data is 0.2 m. In Toulouse, these images are Pléiades tri-stereoscopic satellite images. The GSD of all input data is 0.5 m. Lastly, the DSM used in Lyngby was derived from first pulse LIDAR data, and the digital orthophoto was generated from a scanned aerial image, both with a GSD of 1 m. For the three test areas, up-to-date vector databases representing the 2D outlines of buildings were available. They served as a reference in the test. In order to achieve an objective evaluation, the outdated databases were simulated by manually adding or removing buildings Thus, 107 changes (out of 1300 buildings in the scene) were simulated in Marseille (89 new and 18 demolished buildings); 40 (out of 200) in Toulouse (23 new, 17 demolished) and 50 (out of 500) in Lyngby (29 new, 21 demolished). The outdated databases were converted to binary building masks having the same GSD as the input data and then distributed to the participants along with input data. Each group participating in the test was asked to deliver a change map in which each building of the vector database is labelled either as unchanged, demolished or new. Because the methods have been developed in different contexts, their designs noticeably differ, for instance regarding the definitions of the classes considered in the final change map - e.g. four classes for (Champion, 2007) and six classes for (Rottensteiner, 2008) - and the format of the input data - e.g. vector for (Champion, 2007) and raster for (Matikainen et al., 2007). As a work-around, it was decided to use the building label image representing the updated version of the building map (cf. Section 3) for the evaluation of those methods that do not deliver the required change map in the way described above. Only the method by (Champion, 2007) delivered such a change map, which was also directly used in the evaluation. In order to evaluate the results achieved by the four algorithms, they are compared to the reference database, and the completeness and the correctness of the results (Heipke et al., 1997) are derived as quality measures: TP Complétenos = TF + FN . TP Correctness = TP + FP In Equation 1, TP, FP, and FN are the numbers of True Positives, False Positives, and False Negatives, respectively. They refer to the update status of the vector objects in the automatically-generated change map, compared to their real update status given by the reference. In the case where the final change map is directly used for the evaluation, i.e. with (Champion, 2007), a TP is an object of the database reported as changed (demolished or new) that is actually changed in the reference. A FP is an object reported as changed by the algorithm that has not changed in the reference. A FN is an object that was reported as unchanged by the algorithm, but is changed in the reference. In the three other cases, where a building label image representing the updated map is used for the evaluation, the rules for defining an entity as a TP, a FP, or a FN had to be adapted. In these cases, any unchanged building in the reference database is considered a 77V if a predefined percentage (T h ) of its area is covered with buildings in the new label image. Otherwise, it is considered a FP, because the absence of any correspondence in the new label image indicates a change. A demolished building in the reference database is considered a TP if the percentage of its area covered by any building in the new label image is smaller than T h . Otherwise, it is considered to be a FN, because the fact that it corresponds to buildings in the new label image indicates that the change has remained undetected. A new building in the reference is considered a TP if the cover percentage is greater than T h . Otherwise, it is considered a FN. The remaining areas in the new label image that do not match any of the previous cases correspond to objects wrongly alerted as new by the algorithm and thus constitute FPs. The quality measures are presented in the evaluation on a per- building basis (rather than on a per-pixel basis), as the effectiveness of a change detection approach is limited by the number of changed buildings that is missed or over-detected and not by the area covered by these buildings. As explained in the Section 4, these quality measures are also computed separately for each change class. 3. CHANGE DETECTION APPROACHES The four methods tested in this study are concisely presented, ordered alphabetically according to the corresponding author. Champion, 2007: The input of the method is given by a DSM, CIR orthophotos and the outdated vector database. Optionally, the original multiple images can also be used. The outcome of the method is a modified version of the input vector database, in which demolished and unchanged buildings are labelled and vector objects assumed to be new are created. The method starts with the verification of the database, where geometric primitives extracted from the DSM (2D contours, i.e. height discontinuities) and, optionally, from multiple images (3D segments), are collected for each object of the existing database and matched with primitives derived from it. A similarity score is then computed for each object and used to achieve a final decision about acceptance (unchanged) and rejection (changed or demolished). The second processing stage, i.e. the detection of new buildings, is based on a Digital Terrain Model (DTM) automatically derived from the DSM (Champion and Boldo, 2006), a normalised DSM (nDSM), defined as the difference between the DSM and the DTM, and an above-ground mask, processed from the nDSM by thresholding. Appropriate morphological tools are then used to compare this latter mask to the initial building mask derived from the vector database and a vegetation mask computed from CIR orthophotos and an image corresponding to the Normalised Difference Vegetation Index (NDVI), which results in the extraction of new buildings. Matikainen et al., 2007: The building detection method of the Finnish Geodetic Institute (FGI) was originally developed to use laser scanning data as primary data. In this study, it is directly applied to the input DSM and CIR orthophotos. A raster version of the database (for a part of the study area) is used for training. The method includes three main steps. It starts with segmentation and a two-step classification of input data into ground and above-ground, based on a point-based analyisis followed by an object-based analysis and using the Terrasolid 1 and Definiens 1 2 software systems. This is followed by the definition of training segments for buildings and trees and the classification of the above-ground segments into buildings and trees. This classification is based on predefined attributes and a classification tree (Breiman et al., 1984). A large number of 1 http://www.terrasolid.fi/. Last visited: 30 June 2009. 2 http://www.defmiens.com/. Last visited: 30 June 2009.

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