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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B3, 2012 
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia 
INDEXING OF SATELLITE IMAGES OF DIFFERENT SPATIAL RESOLUTIONS USING 
MULTI FEATURES 
Bin Luo, Shujing Jiang, Liangpei Zhang, Xin Huang 
Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, 
Wuhan Univerisity 
Wuhan, China 
robinlb2002 @ gmail.com 
ABSTRACT: 
In this paper, we investigate the issue of the retrievals of remote sensing images with different spatial resolutions by using texture 
features. Since the texture features extracted on images with different resolutions are not directly comaprable, we use the resolution or 
scale invariance of the texture features to make them comparable. Image retrieval experiments are carried out on the database composed 
by SPOT images at two different resolutions. 
1 INTRODUCTION 
Space agencies have collected databases with hugh amount of re- 
mote sensing images over the last decades. The indexing of the 
images from such databases is a key issue for the space agen- 
cies. Since 1990s, the Content Based Image Retrieval (CBIR) 
approaches have been proposed for the natural image or multi- 
media databases (see (Lew et al., 2006) for a review). The CBIR 
systems, such as the KES (Colapicchioni and ieee, 2004), KIM 
(Datcu et al., 2003), for remote sensing images have also been 
developped. 
One particularity of the remote sensing image databases, when 
compared to the natural images databases, is that they are con- 
sitituted by images with different but known spatial resolutions. 
Classical low-level features, such as Gabor features, GLCM fea- 
tures, wavelet features, etc., extracted from images with different 
resolutions are not directly comparable. Thus the images with 
different resolutions can’t be jointly indexed. Fortunately, many 
feature extraction methods are either scale invariant or resolution 
invariant. For example, the Scale Invariant Feature Transforma- 
tion (SIFT) has been proposed (Lowe, n.d.). The scale invariance 
of the Gabor filter bank has been investigated in (Kamarainen 
et al., 2006). In (Luo et al., 2008), the authors have proposed 
the resolution invariance for the Gaussian derivative wavelets in 
order to compare the Gaussian wavelet features obtained from 
the images with different resolutions. The invariances of these 
low-level features make it possible to compare the features ex- 
tracted from images with different resolutions, which can hence 
be jointly indexed. 
The main contribution of this paper is to propose the schemes 
for comparing the Gaussian wavelet features, the Gabor features, 
the GLCM features and the shape features by using the reso- 
lution/scale invariances for the joint retrieval of remote sensing 
images at different reslutions. The well-known SIFT features 
have not been investigated in this study due to the fact that the 
SIFT features are proposed for describing local salient points over 
scales, rather than the whole scene containing in the image. 
The paper is organized as follow. In Section 2, we briefly intro- 
duce the features used for indexing the remote sensing images. 
In addition, in the same section, we present how to compare the 
features extracted from the images at different reslutions for the 
indexing. In Section 3, we present the data sets used for experi- 
ments, as well as the parameters used for extracting the features. 
In Section 4, the retrieval results of remote sensing images at dif- 
ferent reslutions are shown. In Section 5, we conclude. 
2 EXTRACTION OF FEATURES OBTAINED AT 
DIFFERNT RESOLUTIONS 
The radiometric features and the texture features are used for in- 
dexing of remote sensing images at different reslutions. In this 
section, we briefly introduce the methods for extracting the fea- 
tures. Since the low level features extracted on the images at 
different reslutions are not always comparable, in addition, the 
approaches which allow to compare the features extracted from 
images at different reslutions are also proposed for each type of 
features in this section. 
2.1 Radiometric features 
The mean values and the standard deviations of the images are 
computed as radiometric features for indexing. More concretely, 
the radiometric features for an image is defined by: 
Or = {nS}, (1) 
$5,109 
where yp = #45 (I(æ,y) is the gray value of the pixel x 
on the image and N is the number of pixels in an image), 5 = 
| 35, , 0-7? 
N 
If the histogram of the remote sensing images on the same scene 
but with different spatial resolutions are similar, their radiometric 
features can be directly compared without any additional steps. 
2.2 Texture features 
Three classical methods are used for extracting texture features in 
this paper: the continuous Gaussian wavelets and the Gray Level 
Co-ocurrence Matrix (GCLM). 
2.2.1 Gaussian wavelet features The Gaussian scale-space 
representation of an image I is defined as: 
Ly = 1 x ke, (2) 
where k; = =e UE and t is the scale parameter. The 
features of the image /,. (of resolution r) at scale t are computed