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Mapping without the sun
Zhang, Jixian

Liang Shouzhen 3 » Li Lanyong b
a School of Geomatics, Liaoning Technical University ,Fuxin,liangshzh0816@163.com
b School of Geomatics, Liaoning Technical University ,Fuxin,lilanyong@sohu.com
KEY WORDS: Image Fusion, Discrete Wavelet, Multi-spectral, SAR, TM
The fusion between optical image data and SAR data is very important. An important reason is that SAR data can complement the
missing information in optical remote sensing data due to adverse weather condition (rain, cloud cover etc). In this paper, several
image fusion methods were examined. The experimental results indicate conventional methods including PCA (principle component
analysis), IHS (Intensity-Hue-Saturation) and Brovey transform are not efficient for fusing TM optical image and SAR image
because these methods do not take into account the contextual spatial information, while wavelet-based fusion method integrates the
high-frequency components of the higher resolution data with the low-frequency components of the lower resolution data .So this
paper adopts the method of 2-D discrete wavelet transform to fuse the TM data and SAR image data. And in this paper a new fusion
method integrating wavelet and PC is presented. The result shows that the wavelet-based new method improves the high spatial
texture information apparently and preserves the spectral characteristics of original multi-spectral images highly. Compared with
conventional fusion methods(HIS,PCA and Brovey transform) ,this method has some advantage according statistic (mean ,entropy,
standard deviation and correlation ) and better visual result .the output image better preserves the spectral integrity . So it is more
suitable to fuse TM multi-spectral with SAR image data.
With the availability of multi-sensor data in many fields, multi
sensor data fusion has emerged as a new and promising research
area. The purpose of different sensor data is to better identify
natural and manmade objects. Namely the essence of image
fusion is to combine these two kinds of images to form new
images for improving the performances of the fused images in
information content, resolution, and reliability and
interpretation. In the optical remote sensing, some satellite
sensors supply the spectral bands needed to distinguish features
but not spatially due to physical and technological constraints,
while other satellite sensors supply the spatial resolution for
distinguish the features spatially but not spectrally. Besides the
optical remote sensing is seriously affected by weather
condition, for example rain, cloud coverage etc. But the radar
which is all weather and all time is free of weather condition.
So in order to improve the data quality, the fusion of multi
sensor image is very important, especially the fusion of optical
remote sensing data and radar data.
Presently many image fusion methods have been proposed for
combining remote sensing data .A detailed review on this issue
was given by Pohl and Van Genderen (Pohl et al, 1998).The
well-known methods are, for example, the HIS (Intensity, Hue,
Saturation) (Edwards et al, 1994), PCA (Principal components
Analysis) (Zhou, 1998; Chavez, 1989), Brovey Transform
(Gillespie et al 1987) .These methods are conventional methods
which have been studied widely. If the objective of image
fusion is to construct synthetic images that is closer to the
reality they represent ,it is no wonder that conventional fusions
may be used to merge the remote sensing data successfully, for
example SPOT Pan and multi-spectral data. But these fusion
methods have some deficiencies. The most serious problem is
that it leads to some distortion of spectral characteristics.
Recently developments in wavelet analysis provide a potential
solution to these drawbacks. For example, Nunez developed an
approach to fuse a high-resolution panchromatic image with a
low-resolution multi-spectral image based on wavelet
decomposition (Nunez et al, 1999). Ranchin and Wald designed
the ARSIS concept for fusing high spatial and spectral
resolution images based on the multi-resolution analysis of two-
band wavelet transformation (Ranchin et al, 2000).Based on the
virtue of wavelet-based fusion, we adopt this method to merge
high resolution radar image and low resolution optical image in
this paper. In addition, we carry on comparison of fusion
methods according to the criteria proposed by Wald et al (Wald
et al, 1997).
The structure of this paper is as follows. The first section is
introduction .In this section mainly introduce the study on
image fusion and the structure of the whole paper. The next
section discusses the theoretical basis and transformation
characteristics of 2-band wavelet. Then the fusion method based
on 2-band discrete wavelet is presented. This is followed by a
discussion of the image fusing experiment. Next, the
experimental result is compared with previous methods
developed for image fusion, such as IHS method and PCA
Corresponding author iLiang Shouzhen ,School of Geomatics, Liaoning Technical University ,Fuxin, 123000
E-mail: ,liangshzh0816@ 163 .com