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INDEXING OF MID-RESOLUTION SATELLITE IMAGESWITH STRUCTURAL 
ATTRIBUTES 
A. Bhattacharya a,b , M. Roux a , H. Maître 3 ,1. Jermyn b , X. Descombes b , J. Zerubia b 
“Institut TELECOM, TELECOM ParisTech, CNRS UMR 5141 LTCI,75013 Paris, France - 
(avik.bhattacharya, michel.roux, henri.maitre)@telecom-paristech.fr 
b Ariana (joint research group INRIA/I3S) INRIA, BP 93, 06902 Sophia Antipolis, Cedex France - 
(Ian.Jermyn, Xavier.Descombes, Josiane.Zerubia)@inria.fr 
Commission IV, WG IV/2 
KEY WORDS: Landscape, Segmentation, Features, Extraction, Classification, Modelling, Data Mining 
ABSTRACT: 
Satellite image classification has been a major research field for many years with its varied applications in the field of Geography, 
Geology, Archaeology, Environmental Sciences and Military purposes. Many different techniques have been proposed to classify 
satellite images with color, shape and texture features. Complex indices like Vegetation index (NDVI), Brightness index (BI) or 
Urban index (ISU) are used for multi-spectral or hyper-spectral satellite images. In this paper we will show the efficiency of 
structural features describing man-made objects in mid-resolution satellite images to describe image content. We will then show the 
state-of-the-art to classify large satellite images with structural features computed from road networks and urban regions extracted 
on small image patches cut in the large image. Fisher Linear Discriminant (FLD) analysis is used for feature selection and a one-vs- 
rest probabilistic Gaussian kernel Support Vector Machines (SVM) classification method is used to classify the images. The 
classification probabilities associated with each subimage of the large image provide an estimate of the geographical class coverage. 
1. INTRODUCTION 
The growth of large image databases during the last few 
decades with the advancement in image acquisition 
technologies have attracted researchers from different fields to 
work in the domain of image information mining systems. 
These images coming from various sources must be 
systematically analyzed to render important information which 
are often less relevant to human perception. The technologically 
advanced satellite sensors and the new storage systems have 
made image data too vast and complex. The manual annotation 
to describe a complex image completely is not feasible. 
Indexing and retrieval from remote sensing image databases 
relies on the extraction of appropriate information from the data 
about the entity of interest (Daschiel and Datcu, 2005). 
Indexing satellite images (Maitre, 2007) depends on the choice 
of features which in turn are dependent on the type and 
resolution of the sensors. For instance SIFT descriptors are 
widely used in the domain of multimedia (Lowe, 2004). 
Complex indices like Vegetation index (NDVI), Brightness 
index (BI) or Urban index (ISU) are used for multi-spectral or 
hyper-spectral images. Texture features are known to be highly 
discriminative for low resolution panchromatic images 
(Schroeder et al., 1998). Structural features describing man 
made objects in mid-resolution images are most efficient to 
describe image content (Bhattacharya et al., 2007). The road 
network contained in an image is one example. The properties 
of road networks vary considerably from one geographical 
environment to another. The structural features computed from 
them can therefore be used to classify and retrieve such 
environments (Bhattacharya et al., 2007). In order to compute 
the structural features of the road network, we first need to 
extract the road network from the image and then convert the 
output to an appropriate representation. This representation 
must be absolutely independent of any extraction method. The 
road extraction methods are in general resolution dependent. An 
optimal road network extraction algorithm to accurately 
delineate road structures for all practical purposes is very hard 
to achieve. The methods used in our study are robust on many 
such road characteristics but they often failed to extract the 
narrow and finely structured road networks which are almost 
hidden in small urban areas. This failure of the extraction 
methods and hence the features computed from road networks 
poorly classify images containing such areas. In order to obtain 
some meaningful information from these regions, we need to 
segment such areas occurring in the images. A new set of 
structural features computed on segmented urban areas 
combined with the existing road network features provided an 
improved classification of the geographical environments. 
In images, pixels provide the most basic level of information. 
The pixel values are the measurements of the satellite sensors of 
a region on the Earth surface. The information from these pixels 
are at a level far below the semantic meaning of the desired 
object or region. The classification of images based on the pixel 
values is tedious and expensive and hence is not an efficient 
strategy. In this paper we present a novel methodology to 
classify large satellite images with patches of images extracted 
from them. This is a novel idea in the sense that the patches 
considered contain a significant coverage of a particular type of 
geographical class. A one-vs-rest probabilistic Gaussian kernel 
Support Vector Machines (SVM) classification method is used 
to classify the images. In the work presented in this paper we 
have defined 7 such classes. These classes can be categorized as 
follows: 2 urban classes consisting of “Urban USA” and “Urban 
Europe”; 3 rural classes consisting of “Villages”, “Mountains” 
and Fields; an “Airports” class and a “Common” class (this can 
be considered as a rejection class indicating in particular images 
from seas).