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