XXII ISPRS Congress 2012: Technical Commission VII

Bibliographic data

Multivolume work

Persistent identifier:: 1663813779

Title:: XXII ISPRS Congress 2012

Sub title:: Melbourne, Australia, 25 August-1 September 2012

Year of publication:: 2013

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663813779

Language:: English

Additional Notes:: Kongress-Thema: Imaging a sustainable future

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Document type:: Multivolume work

Volume

Persistent identifier:: 1663821976

Title:: Technical Commission VII

Scope:: 546 Seiten

Year of publication:: 2013

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663821976

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(39,B7)

Language:: English

Additional Notes:: Erscheinungsdatum des Originals ist ermittelt.; Literaturangaben

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2019

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: [VII/5: METHODS FOR CHANGE DETECTION AND PROCESS MODELLING]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: OBJECT-BASED CHANGE DETECTION USING HIGH-RESOLUTION REMOTELY SENSED DATA AND GIS N. Sofina, M. Ehlers

Document type:: Multivolume work

Structure type:: Chapter

^ T zT x OF *ÍDM O 2 Za $1DM, 45 7006 2 ii *iDM 90 ZONE i 8IDM, 135 00: — s [DM meat ü #IDM 6 SIM af *IDM 9 *IDOM 138 5 XM, menn. FS sta 9:0 E» wiz ai 24 ; 925 S26 927 Destroved buildings Figure 4. Randomly selected objects present the variation of textural features for intact (top) and destroyed (bottom) buildings. Thus, using maximum and minimum of the angular textural features, we can exploit the presence of the textural orientation for building condition identification. The use of the average values of the angular features reduces this phenomenon. The selection of features is based on the analysis of the frequency distribution histograms (Figure 5). Consequently, the maximum value of the angular features for homogeneity (IDM max) is taken for the classification together with the DPC. pppoe C BE 189 as eo DP vaine Figure 5. Frequency distribution histograms of the selected features. Red values correspond to destroyed buildings, blue values to intact buildings. 3.4 Classification After selection of the most relevant features the building condition is calculated as a binary classification. There are many classification techniques implemented in the Open Source data mining software ‘Orange’. However, we are of the opinion that an optimally selected feature set is an indispensable condition for a successful classification. Unlike supervised techniques, the utilization of unsupervised classification does not require a suitable training data set which is difficult to adapt for all types of constructions. Reasoning from this assumption we use an unsupervised k-means clustering with the Euclidean distance in our study. The main rational is the simplicity of its implementation and its performance and applicability even on large data sets. For the clustering, the centres of cluster are initially chosen arbitrarily. Then every point is assigned to the cluster according to a similarity measurement, for example, distance. The centres are recomputed as centres of mass of their assigned points. The algorithm comes to an end, when there are no changes by the next iteration or when the number of changes is below a given threshold. 4. RESULTS AND DISCUSSION The developed change detection algorithm is evaluated using remotely sensed image data acquired after the Yushu earthquake International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012 XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia on April 2010 and a related vector map containing 610 objects corresponding to the pre-event urban situation. After the feature extraction process and the analysis of the calculated features the building conditions are calculated by an unsupervised k-means clustering. The following confusion matrix shows the result of the experiment. Prediction CC Cl | 175 93 268 C2 | 40 302 | 342 215 | 395 | 610 Correct class Table 2. Confusion matrix. C1 — intact buildings, C2 —destroyed buildings. The classification accuracy of 78% demonstrates the ability of this method to detect destroyed buildings. The independent features that depict the different types of the information are prerequisite to the sufficient result of the analysis. Furthermore, the application of the maximum value of the angular textural features instead of the generally used average values leads to a significant improvement. Considering the intact and damaged building separately, we observe that average values decreases feature performance and consequently the change detection accuracy. Detailed analysis of the results shows that the main classification errors are due to the image quality and rectification errors; for example, shadow from a building covering another one, not well defined destruction that cannot be recognized as a heterogeneity, and incorrect position of vector objects. The object-oriented GIS technology makes it possible to concentrate on the investigation of specified objects, thereby reducing the false alarms which are due to natural changes in the environment and not to buildings destruction. A subset of the generated damage map is shown in Figure 6. Figure 6. Change detection result by the proposed method. Red polygons indicate destroyed building, blue polygons intact buildings. 5. SOFTWARE ENVIRONMENT The methodology is implemented using Open Source software components. The processing of vector and raster data sets including vector data selection and conversion, and data visualization is performed in the GIS GRASS (Geographic Resources Analysis Support System) environment (Neteler & Mitasova, 2004). It is currently the most popular system among

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