Multi-resolution and multi-spectral image fusion for 
urban object extraction 
Yun Zhang and Ruisheng Wang 
Department of Geodesy and Geomatics Engineering 
University of New Brunswick 
15 Dineen Dr., Fredericton, New Brunswick, CANADA E3B 5A3 
YunZhang@UNB.ca; «Ruisheng Wang? i2z51(gunb.ca 
XX"' ISPRS Congress 
KEYWORDS: Digital, Urban, Object, Multispectral, Fusion, Classification, Extraction 
ABSTRACT: 
A new approach for object extraction from high-resolution satellite images is presented in this paper. The new approach integrates 
image fusion, multi-spectral classification, feature extraction and feature segmentation into the object extraction of high-resolution 
satellite images. Both spectral information from multispectral (MS) images and spatial information from panchromatic (Pan) images 
are utilized for the extraction to improve accuracies. This paper mainly concentrates on road extraction from QuickBird MS and Pan 
images using the proposed approach. Experiments of road extraction with QuichBird MS and Pan images demonstrate that the 
proposed approach is effective. The completeness and correctness of road network extraction reaches 0.95, significantly higher than 
those of other existing road extraction methods. 
1. INTRODUCTION 
The latest high-resolution satellites, such as IKONOS, 
QuickBird and OrbView, all simultaneously collect multi- 
spectral (MS) images at a lower resolution (4m or 2.8m) and 
panchromatic (Pan) images at a higher resolution (1m or 0.7m). 
In the multi-spectral images, most urban objects can be visually 
recognized according to the colour difference, but they cannot be 
clearly delineated due to the lack of spatial resolution. In the 
panchromatic images, however, the shape of most individual 
objects can be clearly identified, but many of them cannot be 
classified due to the lack of spectral information. 
To extract useful information from available high-resolution 
images, including airborne and spaceborne imagery, different 
automatic and semi-automatic approaches have been developed. 
To date, automatic techniques for information extraction from 
imagery can be divided into two main categories: (1) multi- 
spectral classification techniques to classify objects from multi- 
spectral images, and (2) grey value and feature based techniques 
to extract objects from panchromatic images. 
Multi-spectral classification techniques are efficient for 
classifying homogeneous objects. But they are inefficient for the 
classification of urban objects when high-resolution images are 
used, because many urban objects are not homogeneous in 
colour and different objects may appear in similar or same 
colour, such as asphalt paved roofs and roads. Further, the 
classification results usually are coarse due to the lower 
resolution of the multispectral images. 
On the other hand, grey value and feature based techniques have 
been successful in extraction of objects from some simple 
panchromatic scenes. But they are still very limited in the 
extraction of urban objects due to the complexity of urban 
scenes and due to the lack of spectral information. In urban areas 
different objects may appear in similar grey values and have 
960 
similar shapes, such as building roofs and small parking lots, 
so that they can be hardly differentiated automatically 
according to the grey values and shapes. 
In this paper, we present a new approach to better extract 
urban objects, which utilizes both spectral information from 
multi-spectral images and spatial information from 
panchromatic images. Different from existing techniques, this 
new approach effectively integrates techniques of image 
fusion, multi-spectral classification, and feature extraction into 
the extraction process. New image fusion and edge-aided 
classification algorithms and software have been developed. 
The concept of the new approach can be used for urban road 
network extraction, building extraction, and other object 
extractions. Here we mainly concentrate on road extraction 
using QuickBird multispectral and panchromatic images. The 
road extraction results demonstrated that this new approach is 
significantly superiors to the conventional multi-spectral 
classification, panchromatic image based feature extraction, 
and other existing multi-spectral and panchromatic image 
integrated classifications. 
2. THE PROPOSED APPROACH 
Figure 1 illustrates the general process of the proposed 
approach. To overcome the shortcoming of classification of 
low-resolution MS images, the MS and Pan QuickBird images 
are first fused into a pan-sharpened MS image. An 
unsupervised classification is then applied to the pan- 
sharpened image to obtain a classified road image. And an 
edge detection approach is applied to the Pan image to obtain 
an edge image. In the edge-aided segmentation, the binary 
edge image from the Pan image is employed to segment the 
classified road image from the pan-sharpened image. Then, a 
shape-based segmentation and a segments filtering algorithm 
are employed to remove non-road objects. 
     
   
  
  
   
  
  
  
  
  
  
  
   
   
  
  
  
  
  
  
   
  
  
   
  
  
  
   
  
  
  
  
   
  
  
  
  
  
   
   
  
   
  
  
   
  
  
  
  
   
  
  
  
  
   
  
  
  
    
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