International Journal of Applied Earth Observation and Geoinformation
Wavelet-based image fusion and quality assessment
Introduction
In recent years, the launch of high-resolution satellites such as IRS-1C/1D, IKONOS, QuickBird, SPOT 5 has opened a new era for remote sensing and photogrammetry. A recent research focus for remote sensing is the development of methods for applying these high-resolution satellite imageries in different fields.
With these remote sensors, images of various spatial and spectral characteristic can be obtained. For example, from the IKONOS sensor, both 1 m resolution panchromatic and 4 m resolution multi-spectral images are available. With the high spatial resolution panchromatic image, detailed geometric features can easily be recognized, while the multi-spectral images contain rich spectral information. The capabilities of the images can be enhanced if the advantages of both high spatial and spectral resolution can be integrated into one single image. The detailed features of such an integrated image can thus be easily recognized and will benefit many applications, such as urban and environmental studies. Image fusion is one of the techniques, which can be used to generate this type of images.
Many methods have been developed for fusing images. These include for example, the IHS, HLS, COS and HSV fusion method (Zhang, 1999, Li and Liu, 1998, Ehlers, 1991). The wavelet analysis method provides an alternative method for remotely sensed image fusion. It has been a recent research focus among several proposed solutions. Bruno et al. employed two different tools originally used in signal processing: multi-resolution analysis and two-band wavelet transformation (Bruno et al., 1996). Nunez et al. developed an approach to fuse a high-resolution panchromatic image with a low-resolution multi-spectral image by wavelet method (Nunez et al., 1999). Sun et al. studied the fusion of remotely sensed imageries based on characteristics of wavelet transformation (Sun et al., 1998). Ranchin and Wald developed the ARSIS concept for fusing high spatial and spectral resolution imagery based on wavelet analysis (Ranchin and Wald, 2000). Similar researches have also been conducted by others (Chibani and Houacine, 2003, Simone et al., 2002). Further contribution to the two-band wavelet transformation for image fusion has been to extend it to multi-band wavelet-based image fusion. Addressing the problem of the ratio of the spatial resolutions of the images to be fused, Shi et al. initiate a method to fuse panchromatic SPOT and multi-spectral TM images by three-band wavelet transformation (Shi et al., 2003). Furthermore, Shi et al. proposed a method for fusing one meter resolution panchromatic and four meter resolution multi-spectral IKONOS imageries based on four-band wavelet transformation (Shi et al., 2005). Concerning the various methods developed for fusing various satellite images, it is necessary to give a general assessment and analysis of the fusion methods, and furthermore to assess the quality of the fused images. The result of such an analysis is then normally used as a reference for selecting the fusion method for image fusion. This paper focuses on two issues: (a) a review and analysis of various fusion methods, especially multi-band wavelet-based method and (b) quality assessment of fused images.
The remainder of this paper is organized as follows. Section 2 introduces the characteristics of wavelet, especially multi-band wavelet transformation. Section 3 reviews and analyzes the methods for image fusion, such as IHS and wavelet methods, particularly the multi-band wavelet-based image fusion methods. Section 4 describes quality assessment methods for image fusion. Furthermore, the indicators are applied to assess the fusion methods in Section 5. Finally, conclusions and recommendations are given.
Section snippets
Characteristics of wavelet transformation
Wavelet analysis was invented in 1980, and since then many studies of both theoretical and application aspects of wavelet analysis have been conducted. Applications of wavelet analysis are potentially extensive and the technique has been used in many different scientific and application fields with great success. Wavelet analysis has been greatly successful in the area of geo-spatial information, such as texture analysis of satellite images, generalization (reduction) of DEMs, image fusion,
Image fusion methods
In this section, we review and analyze image fusion methods that can be used for high-resolution satellite image fusion, such as those for fusion of panchromatic and multi-spectral images. Three categories of image fusion methods are addressed: (a) HIS—a commonly utilized approach for image fusion, (b) two-band wavelet method, and (c) multi-band wavelet method. Assessment of the fused images based on these methods is given in the next section.
Quality indicators for assessing image fusion
The purpose of image fusion is to enhance the spatial and spectral resolution from several low-resolution images. It is thus necessary to propose quality indicators to measure the quality of the images generated by different image fusion methods. Up to now, several indices have been proposed for assessing image quality, which can also be applied to assessing the quality of a fused image. Generally, image assessment methods can be divided into two classes: firstly qualitative (or subjective)
A comparison between three-band wavelet based fusion with two-band wavelet and IHS method
In Section 3, we introduced fusion methods based on the two-band wavelet transformation, three-band wavelet transformation and the four-band wavelet transformation. This section assesses fusion results obtained by these methods and IHS. Here, we use the images in Section 3 (Fig. 2, Fig. 3) as examples. Results are shown in Table 3, Table 4, respectively.
From Table 3, we observe that the values of several quality indices obtained by three-band wavelet transformation fusion method are all much
Conclusions
This paper addressed two issues: (a) an analysis of image fusion methods and (b) quality assessment for images fused using different methods.
According to the transformation characteristics of wavelet and ratio of the resolutions of the images to be fused, we conclude that the multi-band wavelet fusion method can be more widely used than the two-band wavelet image fusion method. For example, we can fuse a 1 m resolution IKONOS panchromatic image and 4 m multi-spectral images by four-band wavelet
Acknowledgements
This work was supported by the funds from Research Grants Council of the Hong Kong SAR (Project No. PolyU5167/03E and PolyU5166/03E, 3-ZB40), The Hong Kong Polytechnic University research fund (Project No. 1.34.9709).
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