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 
    
NEW COMBINED PIXEL/OBJECT-BASED TECHNIQUE FOR EFFICIENT URBAN 
CLASSSIFICATION USING WORLDVIEW-2 DATA 
Ahmed Elsharkawy ^", Mohamed Elhabiby *° & Naser El-Sheimy ^? 
"Dept. of Geomatics Engineering, University of Calgary, Calgary, Alberta, T2N 1N4 
Phone: 403-210-7897, Fax: 403-284-1980, 
"Email: askelsha(@Ducalgary.ca 
‘Public Works Department, Faculty of Engineering, Ain Shams University, Cairo, Egypt 
Email: mmelhabi@ucalgary.ca 
4 Email: elsheimy@ucalgary.ca 
Commission VII/A 
KEY WORDS: High resolution satellite imagery, Pixel/ Object-based, Curvelet transform, Edge detection, Band ratio, Building 
detection. 
ABSTRACT 
The new advances of having eight bands satellite mission similar to WorldView-2, WV-2, give the chance to address and solve some 
of the traditional problems related to the low spatial and/or spectral resolution; such as the lack of details for certain features or the 
inability of the conventional classifiers to detect some land-cover types because of missing efficient spectrum information and 
analysis techniques. High-resolution imagery is particularly well suited to urban applications. High spectral and spatial resolution of 
WorldView-2 data introduces challenges in detailed mapping of urban features. Classification of Water, Shadows, Red roofs and 
concrete buildings spectrally exhibit significant confusion either from the high similarity in the spectral response (e.g. water and 
Shadows) or the similarity in material type (e.g. red roofs and concrete buildings). 
This research study assesses the enhancement of the classification accuracy and efficiency for a data set of WorldView-2 satellite 
imagery using the full 8-bands through integrating the output of classification process using three band ratios with another step 
involves an object-based technique for extracting shadows, water, vegetation, building, Bare soil and asphalt roads. Second 
generation curvelet transform will be used in the second step, specifically to detect buildings’ boundaries, which will aid the new 
algorithm of band ratios classification through efficient separation of the buildings. The combined technique is tested, and the 
preliminary results show a great potential of the new bands in the WV-2 imagery in the separation between confusing classes such as 
water and shadows, and the testing is extended to the separation between bare soils and asphalt roads. The Integrated band ratio- 
curvelet transform edge detection techniques increased the percentage of building detection by more than 30%. 
1. INTRODUCTION 
The processes of per-pixel supervised classification methods 
were always the primary tool to extract land cover classes from 
digital remotely sensed data (Bhaskaran et al, 2010). The 
ultimate goal of any image classification procedure is to 
automatically categorize all pixels in an image into land cover 
classes (Lillesand and kiefer, 2001). For the purpose of urban 
planning, supervised classification has been used extensively. 
Unfortunately, this procedure always results in mixed pixel’s 
problem (Bhaskaran et al, 2010). This problem leads many 
researchers to incorporate segmentation, texture, context, colour, 
and many other parameters to glide the mixed or wrongly 
classified pixels into their proper classes. Segmentation can be 
done either by detecting similarities or by detecting singularities 
(edge detection) (Gonzalez and Woods, 2002). Contrasting 
spectral methods, object-oriented methods are based on 
segmenting the image into homogeneous parcels of pixels then 
these parcels are classified using spectral, spatial, textural, 
relational and contextual methods (Bhaskaran et al., 2010). 
The primary objective of this study was to classify urban 
features from a WorldView-2 imagery by using both per-pixel 
classification (three new band ratios), and object-oriented 
classification method (edge detection using curvelet transforms). 
The first approach was to use the three new band ratios to 
classify the image and check accuracy of all classes. The 
following approach was to improve the accuracy of the lowest 
two classes’ accuracy. 
In 2009 Digital Globe launched the WorldView-2 satellite. It is 
the first commercial high-resolution satellite to provide eight 
spectral sensors in the visible to the near-infrared range. Each 
sensor closely focuses on a particular range of the 
electromagnetic spectrum which is sensitive to a specific feature 
on the ground and was designed to improve the segmentation 
and classification of land and marine. Figure 1 is a concise 
comparison between QuickBird, IKONOS, WorldView-1 and 
WorldView-2 of their spectral and panchromatic bands. 
In addition to its large-scale collection capacity, WorldView-2 
has a high spatial and spectral resolution. According to (Globe, 
2009) this satellite can capture a 46 cm panchromatic image 
with 1.84 m spectral resolution with 8-band multispectral 
imagery. The high spatial resolution facilitates the 
differentiation of fine details like small and medium-size 
buildings, shallow reefs and individual trees while the high 
spectral resolution provides detailed information about diverse