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 (a) Matikainen et al. (2007) (b) Rottensteiner (2008) (c) Olsen and Knudsen (2005) Figure 1. Evaluation of change detection in Lyngby, for (a), (b) and (c) Green: TP; red: FN; orange: FP; blue: TN. Unchanged Demolished New (Champion, 2007) Completeness [%] 93.5 88.9 95.2 Correctness [%] 99.8 18.4 63.5 (Matikainen et al., 2007) Completeness [%] 94.7 100 97.6 Correctness [%] 100 23.7 75.6 (Rottensteiner, 2008) Completeness [%] 94.1 100 94.0 Correctness [%] 100 22.0 96.3 Table 2. Completeness and correctness for the Marseille test area, depending on the update status. | Unchanged \ Demolished \ New (Matikainen et al., 2007) Completeness [%1 81.7 100 91.8 Correctness \%] 100 22.6 100 (Olsen and Knudsen, 2005) Completeness [%1 87.8 100 93.9 Correctness [%] 100 30.4 82.1 (Rottensteiner, 2008) Completeness [%] 95.9 100 87.8 Correctness [%) 100 56.8 91.8 Table 3. Completeness and correctness for the Lyngby test area, depending on the update status. Unchanged Demolished New (Champion, 2007) Completeness f%j 82.8 100 75.0 Correctness \%] 100 42.9 65.2 (Rottensteiner, 2008) Completeness [%] 80.2 86.7 82.6 Correctness [%] 97.9 36.1 59.4 Table 4. Completeness and correctness for the Toulouse test area, depending on the update status. ranges from 18.4% with (Champion, 2007) to 23.7% with (Matikainen et al., 2007). The situation is a bit better for new buildings, with a correctness rate larger than 63% for all the methods and even rising to 96.8% with (Rottensteiner, 2008). In spite of such limitations, all the methods presented here are very efficient in classifying unchanged buildings, for which the completeness rates are higher than 93%, which indicates that a considerable amount of manual work is saved and also demonstrates the economical efficiency of these approaches in the context of aerial imagery. Analyzing Table 3 leads to similar conclusions for the LIDAR context. The correctness rate for the reported demolished buildings are again poor and only (Rottensteiner, 2008) achieves less than 50% false positives. However, the methods are very effective in detecting demolished buildings and achieve a completeness rate of 100% for this class. Compared to the outcomes obtained in Marseille, the main difference concerns the new buildings, which appear to be more difficult to extract. Thus, between 6.1% (Olsen and Knudsen, 2006) and 12.2% (Rottensteiner, 2008) of the new buildings are missed. If these percentages of missed new buildings can be tolerated, our tests indicate that LIDAR offers a high economical effectiveness and thus may be a viable basis for a future application. If these error rates for new buildings are unacceptable, manual post-process is required to find the missed buildings, at the expense of a lower economical efficiency. The situation is not quite as good with the satellite context (Table 4). The method by (Champion, 2007) is very effective in detecting demolished buildings (100%), but this is achieved at the expense of a low correctness rate (42.9%). The same analysis can be carried out with (Rottensteiner, 2008), but this method even misses quite a few demolished buildings. It has to be noted that, even though the completeness rates for unchanged buildings achieved by both methods are relatively low compared to those obtained in the Marseille and Lyngby test areas, they also indicate that even under challenging circumstances, 80% of unchanged buildings need not be investigated by an operator. The main limitation appears to be the detection of new buildings. As illustrated for an example in Figures 3e and 3f, 17.4% and 25% of new buildings are missed with (Rottensteiner, 2008) and (Champion, 2007) respectively, which is clearly not sufficient to provide a full update of the database and requires a manual intervention in order to find the remaining new buildings. In order to obtain deeper insights into the reasons for failure, in the subsequent sections we will focus our analysis on some factors that affect the change detection performance. 4.2 Impact of the Size of a Change To analyse the performance of change detection as a function of the change size, we compute the completeness and correctness rates depending on this factor. For that purpose, new and demolished buildings are placed into bins representing classes 148

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