Special Section on Airborne Hyperspectral Remote Sensing of Urban Environments

Combining data mining algorithm and object-based image analysis for detailed urban mapping of hyperspectral images

[+] Author Affiliations
Alireza Hamedianfar

Universiti Putra Malaysia (UPM), Department of Civil Engineering, Faculty of Engineering, Serdang, Selangor 43400, Malaysia

Helmi Zulhaidi Mohd Shafri

Universiti Putra Malaysia (UPM), Department of Civil Engineering, Faculty of Engineering, Serdang, Selangor 43400, Malaysia

Universiti Putra Malaysia (UPM), Geospatial Information Science Research Centre (GISRC), Faculty of Engineering, Serdang, Selangor 43400, Malaysia

Shattri Mansor

Universiti Putra Malaysia (UPM), Department of Civil Engineering, Faculty of Engineering, Serdang, Selangor 43400, Malaysia

Universiti Putra Malaysia (UPM), Geospatial Information Science Research Centre (GISRC), Faculty of Engineering, Serdang, Selangor 43400, Malaysia

Noordin Ahmad

National Space Agency, Bangunan Komersil PJH, Putrajaya 62570, Malaysia

J. Appl. Remote Sens. 8(1), 085091 (Aug 27, 2014). doi:10.1117/1.JRS.8.085091
History: Received April 30, 2014; Revised July 12, 2014; Accepted August 5, 2014
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Abstract.  Image classification of roofing types, road pavements, and natural features can assist land-cover maps in further examining the effects of such features on health, pollution, and the microclimate in urban settings. Airborne hyperspectral sensors with high spectral and spatial resolutions can be employed for detailed characterization of urban areas. This study aims to develop a procedure that is instrumental for automated knowledge discovery and mapping of urban surface materials from a large feature space of hyperspectral images. Two different images over Universiti Putra Malaysia (UPM) and Kuala Lumpur (KL), Malaysia, were captured by using hyperspectral sensors with 20 and 128 bands. The images were used to explore the combined performance of a data mining (DM) algorithm and object-based image analysis (OBIA). A large number of attributes were discovered with the C4.5 DM algorithm, which also generated the classification model as a decision tree. The UPM and KL classified images achieved 93.42 and 88.36% overall accuracy. The high accuracy of object-based classification can be linked to the knowledge discovery produced by the DM algorithm. This algorithm increased the productivity of OBIA, expedited the process of attribute selection, and resulted in an easy-to-use representation of a knowledge model from a decision tree structure.

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© 2014 Society of Photo-Optical Instrumentation Engineers

Citation

Alireza Hamedianfar ; Helmi Zulhaidi Mohd Shafri ; Shattri Mansor and Noordin Ahmad
"Combining data mining algorithm and object-based image analysis for detailed urban mapping of hyperspectral images", J. Appl. Remote Sens. 8(1), 085091 (Aug 27, 2014). ; http://dx.doi.org/10.1117/1.JRS.8.085091


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