You are using an outdated browser that does not fully support the intranda viewer.
As a result, some pages may not be displayed correctly.

We recommend you use one of the following browsers:

Full text

Mapping without the sun
Zhang, Jixian

Shen Jin-li, Yu Wu-yi, Qi Xiao-ping, Zhang Yi-min
The research institute of petroleum exploration and development
Shenjili Jc@ 163 .com
KEYWORDS: Synthetic aperture radar, road detection, fusion method, interpretation
Due to SAR images features of all day obtained and its penetration ability, we apply fusion method of radar image and
multi-spectrum images, in order to improve the accuracy of road interpretation in the area of densely covered by high vegetation.
Two methods for fusion SAR and TM multi-spectral images are presented in this paper. The experimental results show that the
method of Principle Component Analysis (PCA) conversion fusion is more effective than the method of HIS fusion in the study area.
Synthetic aperture radar (SAR) images not only have the
characteristics of all-day, all-weather, but also provide object
information which is different from visible and infrared sensors.
SAR images are widely applied in many fields, such as
agriculture, forestry, geology, hydrology etc. With high
resolution and no spectral information, it requires professional
experiences to interpret SAR images. Though optical images
have abundant spectral information, it may disturb us to extract
tiny objects. Remotely sensed image fusion has been proved to
be a good method in feature extraction to avoid the faults of
SAR images more speckles and fewer bands.
Adopting a compound framework, image fusion enhancement
integrates data of various sensors to get high quality
information. Data fusion can take advantage of the use of
complementary information to obtain a better overall accuracy
than using single data source only. On the basis of previous
work, we can reduce the uncertainty; improve the accuracy and
reliability of the experimental results. There are several
methods of image fusion such as HIS conversion fusion and
principle conversion fusion, which are based on pixel-level
with aim at improving visual interpretation results or
classification results. There are two main pixel-level methods
to be discussed in this paper, based on Principle Component
Analysis (PCA) and HIS conversion. At last, the maximum
likelihood classifier was used for both fusion images.
It is difficult to extract road from moderate resolution images in
high vegetation region. Synthetic Aperture Radar (SAR) is a
kind of high resolution imaging radar. It can generate images
independent of time and weather condition. It also has the
ability of penetrating through some depth of the earth or
vegetation. We experiment to identify more exact global
information by radar images-ENVISAT and LANDSAT TM
The data sets used in this research includes: 1) LANDSAT TM
acquired on October 11, 2006; with 30m resolution(Figl).
ENVISAT SAR image acquired on October 16, 2006; C wave
band, HH polarization. We applied relief map of 1:50000 and
GPS ground control points surveyed in the field for image
correction and registration. The error is controlled within one
pixel. We denoise the SAR image by Lee filter. This method
not only can reduce speckle noise, but also can remain the edge
features. The size of image is 400X401 pixels. The study area
is in plain with high vegetation. In order to select a more
suitable method applied in the study area, we experiment two
fusion ways: Principle Component Analysis (PCA) conversion
and HIS conversion.
Figl: TM multi-spectral image
Principle Component Analysis-''(PCA) conversion is used to
fuse the TM and SAR images. First, because of the largest
quantity of information in the PC-1 component, we replaced
the PC-5 component by SAR image, different from the usual
processing. Then the inverse transform is followed (Fig2).
Another fusion method is HIS fusion. We transform TM image
from RGB to HIS space, then replace the I-component by SAR
image, finally inverse to RGB space with H- and S-component
(Fig3). At last, the maximum likelihood classifier was used for
both two fusion images and the TM multi-spectral image.
Fig2: image of PCA fusion method