基于多进制小波变换及多维纹理特征的遥感影像融合方法

VN WNs 1 WNs 2 WNs M VN M
Where W , V j are closed subspace column.
s j
(1)
The closed subspace column Vj is generated from
232
农业工程学报
Wu Yanbin1,2, Fu Meichen3, Li Liangjun4
(1. State Key Laboratory of Coal Resources and Safe Mining, China University of Mining and Technology, Beijing 100083, China; 2. School of Business Administration, Hebei University of Economic and Business, Shijiazhuang 050016, China; 3. School of Land Sciences and Technology, China University of Geosciences, Beijing 100083, China; 4. Public Geological Survey Management Center of Anhui Province, Hefei 230001, China) Abstract: The remote sensing image fusion method based on multi-band wavelet combining with multiple dimension texture features was discussed. In the fusion process, the high resolution panchromatic image and multi-spectral image were decomposed with multi-ary wavelet first and then local variance, local average grads, local energy and local information entropy texture features were extracted. After that the high frequency ingredients of high resolution image and R, G, B bands image by multi-band wavelet transform were fused based on combing multiple criteria of multiple dimension texture features and the high frequency ingredients of high resolution image was formed. The inverse multi-band wavelet transform was done based on low frequency ingredients of R, G, B bands image and new high frequency ingredients separately. Using 3-band wavelet transform, the fusion of 10 m resolution SPOT panchromatic and 30 m resolution TM image in Huainan mine was experimented. Visual interpretation (qualitative evaluation), spectral curve analysis and quantitative evaluation were applied to assess the method. The results show that this method can not only improve the definition and resolution of the multi-spectral image, but also retain the color feature. Key words: remote sensing, image fusion, multi-ary wavelet, multiple dimension texture features, combining multiple criteria doi：10.3969/j.issn.1002-6819.2010.12.039 CLC number：TP751 Document code：A Article ID：1002-6819(2010)-12-0231-06 Wu Yanbin, Fu Meichen, Li Liangjun. Remote sensing image fusion by multi-ary wavelet transform combining multiple dimension texture features[J]. Transactions of the CSAE, 2010, 26(12): 231－236. (in English with Chinese abstract)
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Introduction
Remote sensing image fusion is an important part of remote sensing technology applied in monitoring land use changes and is a major means for intensifying the remote sensing information. Remote sensing image fusion can decrease the incompleteness, multiplicity, and uncertainty which probably exist in interpreting target objects. So it can greatly improve the effectiveness in feature extraction, classification, and target recognition. In recent years, many scholars at home and abroad have applied different methods like IHS (intensity hue saturation) transformation, HPF (high pass filter) transformation, PCA (principal component analysis) transformation, wavelet transform to fuse the panchromatic image of high spatial resolution with the multi-spectral images of low spatial resolution[1-2]. But these fusion rules adopted are often according to only one aspect of the image, so the advantages and disadvantages of
various methods coexist. In this paper, the key technology of the remote sensing image fusion process was analyzed and the method for enhancing monitoring accuracy of land use changes by using the multi-ary wavelet transform model combined with multiple texture information for TM and SPOT image fusion was discussed in detail.
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1.1
Materials and methods
Multi-ary wavelet transform model To fuse the panchromatic image of high spatial resolution with the multi-spectral images of low spatial resolution by wavelet transform (WT) can remedy the data deficiency of a single image and enlarge the data application scope of the both so as to improve the interpretation accuracy. If these images whose resolution ratio is not the integral multiple of 2, the multi-ary wavelet transform should be adopted[3-4]. The orthogonal decomposition in the square integrable function space L2(R) can be acquired based on multi-scale analysis theory[5-7]. If V j 1 W js V j (1 s M 1) ， j Z ，then for every integer N and M>0, the following formula holds
第 26 卷 第 12ansactions of the CSAE