Maximizing Urban Features Extraction from Multi-sensor Data with Dempster-Shafer Theory and HSI Data Fusion Techniques
Keywords:
Data fusion, Feature extraction, urban mapping, Hyperspectral, LiDARAbstract
This paper compares two multi-sensor data fusion techniques – Dempster-Sharfer Theory (DST) and Hue Saturation Intensity (HSI). The objective is to evaluate the effectiveness of the methods interm in space and time and quality of information extraction. LiDAR and hyperspectral data were fused using the two methods to extract urban land scape features. First, digital surface model (DSM), LiDAR intensity and hyperspectral image were fused with HSI. Then the result was classified into five classes (metal roof building, non-metal roof building, tree, grass and road) using supervised classification (minimum distance) and the classification accuracy assessment was done. Second, Dempster Shafer Theory (DST) utilized the evidences available to fuse normalized DSM, LiDAR intensity and hyperspectral derivatives to classify the surface materials into five classes as before. It was found out that DST perform well in the ability to discriminate different classes without expert information from the scene. Overal accuracy of 87% achieved using DST. While in HSI technique, the overal accuracy obtained was 74.3%. Also, metal and non-metal roof types were clearly classified with DST which, does not have a good result with HSI. A fundamental setback of HSI is its limitation to fusion of only two sensor data at a time whereas we could integrate different sensor data with DST. Besides, the time required to select trainimg site for supervised classificition, the accuracy of feature classification with HSI fused data is dependent on the knowledge of the analyst about the scene with the other one. This study shows DST to be an accurate and fast method to extract urban features and roof types. It is hoped that the increasing number of remote sensing technology transforming to era of redundant data will make DST a desired technique available in most commercial image processing software packagesReferences
UN, “Population and Vital Statistics Report,†New York, 2012, 2012.
WorldBank, “United Nations’ World Urbanization Prospects,†The World Bank, 2012. [Online]. Available: http://data.worldbank.org/indicator/SP.URB.TOTL.IN.ZS. [Accessed: 06-Jul-2013].
A. Hamedianfar and H. Z. M. Shafri, “Development of fuzzy rule-based parameters for urban object-oriented classification using very high resolution imagery,†Geocarto Int., no. May, pp. 1–25, Mar. 2013.
N. Nasarudin and H. Shafri, “Development and utilization of urban spectral library for remote sensing of urban environment,†J. Urban Environ. Eng., vol. 5, no. 1, pp. 44–56, Jun. 2011.
L. Fiumi, “Surveying the roofs of Rome,†J. Cult. Herit., vol. 13, no. 3, pp. 304–313, Jul. 2012.
W. Heldens, H. Taubenbo, T. Esch, U. Heiden, and M. Wurm, “Thermal Infrared Remote Sensing,†vol. 17, pp. 475–493, 2013.
P. Hardin and A. Hardin, “Hyperspectral Remote Sensing of Urban Areas,†Geogr. Compass, vol. 7, no. 1, pp. 7–21, Jan. 2013.
P. Shippert, “Why use hyperspectral imagery?,†Photogramm. Eng. Remote Sensing, no. April, pp. 377–379, 2004.
U. Heiden, W. Heldens, S. Roessner, K. Segl, T. Esch, and A. Mueller, “Urban structure type characterization using hyperspectral remote sensing and height information,†Landsc. Urban Plan., vol. 105, no. 4, pp. 361–375, Apr. 2012.
E. Taherzadeh and H. Z. M. Shafri, “Using Hyperspectral Remote Sensing Data in Urban Mapping Over Kuala Lumpur,†IEEE Explor., no. Figure 1, pp. 405–408, 2011.
C. Chisense, M. Hahn, and J. Engels, “Classification of roof materials using hyperspectral data,†Appl. Geoinformatics Soc. Environ., pp. 1–8, 2011.
D. Lemp and U. Weidner, “Improvements of roof surface classification using hyperspectral and laser scanning data,†Proc. URBAN 2005 Work. …, no. 1999, 2005.
A. Misni, G. Baird, and P. Allan, “The Effect of Landscaping on the Thermal Performance of Housing,†in International Review for Spatial Planning and Sustainable Development, 1st ed., Z. SHEN, Ed. Kanazawa, Japan: IRSPSD International, 2013, pp. 31–56.
ENGLERT, “The Advantages of Metal Roofing | General Content,†Englert Inc, 2013. [Online]. Available: http://www.englertinc.com/general-content/the-advantages-of-metal-roofing.html. [Accessed: 16-Jun-2014].
M. O. Idrees, H. Z. M. Shafri, and V. Saeidi, “Assessing Accuracy of the Vertical Component of Airborne Laser Scanner for 3DUrban Infrastructural Mapping,†Int. J. Geoinformatics, vol. 9, no. 3, pp. 21–30, 2013.
A. Brook and R. Richter, “Fusion of hyperspectral images and Lidar data for civil engineering structure monitoring,†in Hyperspectral 2010 Workshop from CHRIS/Proba to PRISMA & EnMAP and beyond, 2010, vol. 2010, no. May, pp. 17–19.
K. Segl, S. Roessner, U. Heiden, and H. Kaufmann, “Fusion of spectral and shape features for identification of urban surface cover types using reflective and thermal hyperspectral data,†ISPRS J. Photogramm. Remote Sens., vol. 58, no. 1–2, pp. 99–112, Jun. 2003.
CLASSIC, “The Benefits of Metal Roofing,†Classic metal roofing systems, 2013. [Online]. Available: http://www.classicmetalroofingsystems.com/about-metal-roofing/benefits/. [Accessed: 16-Jun-2013].
F. Rottensteiner, J. Trinder, S. Clode, and K. Kubik, “Using the Dempster–Shafer method for the fusion of LIDAR data and multi-spectral images for building detection,†Inf. Fusion, vol. 6, no. 4, pp. 283–300, Dec. 2005.
J. Dong, D. Zhuang, Y. Huang, and J. Fu, “Advances in Multi-Sensor Data Fusion: Algorithms and Applications,†Sensors, vol. 9, no. 1, pp. 7771–7784, 2009.
ERDAS, Image Analysis for ArcGIS: Geographic Imaging by ERDAS, 1st ed., no. January. Norcross, USA, USA: ERDAS Incorporation, 2009, pp. 110 – 114.
M. Krzywinski, “Image Color Summarizer,†2011. [Online]. Available: http://mkweb.bcgsc.ca/color_summarizer/?faq#whatare. [Accessed: 26-Jan-2013].
M. Idrees, H. Z. M. Shafri, and V. Saeidi, “Imaging Spectroscopy and Light Detection and Ranging data fusion for urban feature extraction,†Am. J. Appl. Sci., vol. 10, no. 12, pp. 1575–1585, 2013.
H. Wang, J. Liu, and J. C. Augusto, “Mass function derivation and combination in multivariate data spaces,†Inf. Sci. (Ny)., vol. 180, no. 6, pp. 813–819, Mar. 2009.
S. E. Franklin, D. R. Peddle, J. A. Dechka, and G. B. Stenhouse, “Evidential reasoning with Landsat TM, DEM and GIS data for landcover classification in support of grizzly bear habitat mapping,†Int. J. Remote Sens., vol. 23, no. 21, pp. 37–41, 2002.
M. H. Tangestani, “A comparative study of Dempster–Shafer and fuzzy models for landslide susceptibility mapping using a GIS: An experience from Zagros Mountains, SW Iran,†J. Asian Earth Sci., vol. 35, no. 1, pp. 66–73, Jun. 2009.
O. F. Althuwaynee, B. Pradhan, and S. Lee, “Application of an evidential belief function model in landslide susceptibility mapping,†Comput. Geosci., vol. 44, pp. 120–135, Jul. 2012.
Rakowsky and U. Kay, “Fundamentals of the Dempster-Shafer theory and its applications to reliability modeling,†Int. J. Reliab. Qual. Saf. Eng., vol. 14, no. 6, pp. 579–601, 2007.
V. Saeidi, B. Pradhan, M. O. Idrees, and Z. Abd Latif, “Fusion of Airborne LiDAR With Multispectral SPOT 5 Image for Enhancement of Feature Extraction Using Dempster–Shafer Theory,†IEEE Trans. Geosci. Remote Sens., vol. pp, no. 99, pp. 1–9, 2014.
A. Bellenger and S. Gatepaille, “Uncertainty in ontologies: Dempster-Shafer theory for data fusion applications,†arXiv Prepr. arXiv1106.3876, 2011.
F. Rottensteiner, J. Trinder, S. Clode, and K. Kubik, “Building detection by fusion of airborne laser scanner data and multi-spectral images : Performance evaluation and sensitivity analysis,†ISPRS J. Photogramm. Remote Sens., vol. 62, pp. 135–149, 2007.
D. Jiang, D. Zhuang, Y. Huang, and J. Fu, “Survey of multispectral image fusion techniques in remote sensing applications,†www.intechopen.com, 2011. [Online]. Available: http://www.intechopen.com/books/image-fusion-and-its-applications/survey-of-multispectral-image-fusion-techniques-in-remote-sensing-applications. [Accessed: 06-Jun-2014]
Downloads
Published
How to Cite
Issue
Section
License
- Papers must be submitted on the understanding that they have not been published elsewhere (except in the form of an abstract or as part of a published lecture, review, or thesis) and are not currently under consideration by another journal published by any other publisher.
- It is also the authors responsibility to ensure that the articles emanating from a particular source are submitted with the necessary approval.
- The authors warrant that the paper is original and that he/she is the author of the paper, except for material that is clearly identified as to its original source, with permission notices from the copyright owners where required.
- The authors ensure that all the references carefully and they are accurate in the text as well as in the list of references (and vice versa).
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Attribution-NonCommercial 4.0 International that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
- The journal/publisher is not responsible for subsequent uses of the work. It is the author's responsibility to bring an infringement action if so desired by the author.