Region Based Image Fusion Using SVM
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1 Region Based Image Fusion Using SVM Yang Liu, Jian Cheng, Hanqing Lu National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences ABSTRACT This paper presents a novel fusion approach using PCA merger based on multiscale decomposition (MSD), combined with region segmentation and support vector machine (SVM), the result is a high spatial resolution multispectral image from a high resolution chromatic (Pan) image and low resolution multispectral (Ms) images. Principal components analysis (PCA) fusion technique is one of typical fusion methods, and PCA merger based on MSD had been proposed which can obtain better performance. As we know that, in pixel fusion level, the original images are fused as internal region regardless of the contents of images, but in this paper, we perform region segmentation after MSD, because the homogeneous regions have similar features such as color, texture and intensity. Traditionally, various fusion rules can be applied after MSD according to different conditions, however, the crucial problem is which fusion rule should be adopted under given condition, hence we use the SVM to combine the most fusion rules so that can avoid some drawbacks using single fusion rule. To validate our approach, we compare it with several typical fusion approaches, and the best result is obtained using our approach. Keywords: Image fusion, multiscale decomposition, SVM, PCA. INTRODUCTION The objective of image fusion is to combine information from multiple images of the same scene. The result of image fusion is a new image which is more suitable for human vision and machine perception or further image-processing such as segmentation or change detection. In recent years, image fusion has been widely concerned in some research fields, especially in remote sensing. There are two major technical limitations for most satellites collecting high-resolution multispectral image directly: one is the incoming radiation energy to the sensor, the other is the data volume collected by sensor. An effective image fusion technique can meet the requirement for high-spatial and high-spectral resolution in an image simultaneously. To obtain high-resolution multispectral image indirectly, former researchers have carried out many researches on image fusion. Many popular image fusion methods are those based on the intensity-hue-saturation transform and principal component analysis. The main drawback of these methods, frequently called component substitution method, is the distortion of the original spectral information. Chavez proposed the highpass filtering method. In the past few years, several researchers have proposed discrete wavelet transform[], Laplacian pyramid algorithms[2] and à trous wavelet transforms[3], but the fused image obtained by pyramid algorithms is 4/3 as same as the original image, and if obvious difference exists between multisensor images, some speckles may be kept in the fused image when pyramid is reconstructed. Recently, María González-Audícana proposed a new fusion method which combined the IHS or PCA with the wavelet transform[4] to obtain better results. In addition, the remote sensing images always contain a great deal of vegetation, rivers, mountains and urban scene. Moreover, the homogeneous objects have similar features such as color, texture, histogram and some statistical features. Therefore, we can segment remote sensing images to get some homogeneous regions, then we merge the homogeneous regions respectively instead of merging the whole image as one region. In this paper, we propose a fusion approach to use Pan image and Ms image based on former researchers referred above. After PCA merger based on MSD, we segment the original images into several different regions and in each region, we provide N-dimension eigenvectors as the input of the SVM to obtain a classifier by training sample points, then fusion each region. We will give details about our approach following this section.
2 2. REGION BASED IMAGE FUSION APPROACH 2. The traditional PCA merger based on Multiscale decomposition Traditionally, the PCA transform converts intercorrelated multispectral bands into a new set of uncorrelated components. In general, the first principal component (PC¹) collects the most spatial information, while the spectral information is picked up in the other principal components. The main advantage of PCA technique is that an arbitrary number of bands can be used. Then the first principal component is replaced by the Pan image whose histogram is matched with it. At last, the high-resolution multispectral image is determined by performing the inverse PCA transform with the Pan image together with other principal components. However, this direct substitution results in spatial information loss and significant distortion of the original spectral information. The reason is that when PCA transform are applied, the spectral and spatial information of the multispectral image is not completely separated. To improve the quality of the merged image, former researchers performed the MSD to extract the spatial detail of the Pan image which is missing in the multispectral image, then injected the detail into PC¹, and obtained a new PC¹ which preserves both the high spatial information of Pan image and spectral information of multispectral image. Finally, IPCA transform is performed to get fused image. 2.2 Region segmentation using edge information As we know that, remote sensing images usually contain two kinds of objects, one kind is natural object such as vegetation, sea, mountains, earth etc. The other is unnatural object, in other words, is man-made object, for example, vehicles, buildings, roads. These two kind objects can be distinguished by different color, texture, gray level, therefore, an image can be classified into several regions, we can fused the whole image with various fusion rule according to different region rather than fused the image with single fusion rule. Considering different character to each region, fusion based on region can efficiently enhance the fusion performance. In our approach, region segmentation follows the MSD. Region segmentation can be performed on the colored multispectral image using the edge information and a labeling algorithm. Traditional edge detection operators just as Prewitt operator has limitation that their inability to detect accurately edges in high-noise environments, however, we know the remote sensing images sometimes have poor image quality due to the hard imaging condition, so we adopt the edge detection operator described in [5]. The output is a labeled image in which each different value represents different region. Then, resizing the labeled image to the size of low-frequency subimage obtained by MSD, we can carry out the fusion process in each different region. The main advantage of region based fusion is that the points in the same region have homogeneous characters, which make the region based fusion better than fusing the whole image as one region, because it adequately considers the characters of remote sensing images. 2.3 Fused coefficients using SVM From [6], we know many fusion alternatives can be used according to different conditions when we fused the images using MSD method. The crucial problem is which fusion rule we should adopt under various conditions. In this paper, we apply the SVM to generate some good performance classifiers by training the sample points from each region respectively; then through these classifiers we fuse the image according to respective region. For each pixel, we provide N-dimension eigenvectors V as the inputs of the SVM to generate the classifier. V is the difference of the eigenvectors V from Pan image and the eigenvectors V¹ from PC¹ at corresponding position, which can be expressed by V = V V () The reason why we select V by Eq. is that some of these eigenvectors, in fact, are computed according to some fusion rules which are enumerated in [6]. Other eigenvectors represent some feature including texture, entropy, third moment, uniformity, smoothness. Generally, when fusing image with single fusion rule, between the two points from Pan image and PC¹ respectively, we consider the one whose value computed according to certain fusion rule is larger contains more energy in local area so that we should select this one to fill the corresponding position in fused image, therefore, the Eq. can denote which one is better based on there eigenvectors between the two points from Pan image and PC¹. For each region we randomly pick up M (M is determined by size of image) points. For SVM, we define the positive sample points are that more than half of their eigenvectors values are more than zero; otherwise, the points are negative samples.
3 After obtained the classifiers by SVM, we can classify the two points from Pan image and form PC¹ corresponding positions by V. If V is classified as positive sample, we select the pixel value of Pan image point as fused image corresponding position pixel value; Otherwise, we compute the pixel value of fused image at each position p by D ( p) = ω D ( p) + ω D ( p) (2) F The weights ω and ω may depend on the activity levels of the source MSD coefficients and the similarity between the source images at the current position. D ( p ) and D ( p) 2 indicate the pixel value of Pan image and PC¹ at position p respectively. At first, a match measure M ( p ) is defined as a normalized correlation averaged over a neighborhood of p, M( p) = Where ω (,) st is a weight and s S, t T s S, t T ω(,) std( m+ sn, + tkld,,) ( m+ sn, + tkl,,) 2 A ( p) + A ( p) ω(,) st =, S and T are sets of horizontal and vertical indexes that describe the current window, the sums over s and t range over all samples in the window. m and n indicate the spatial position in a given frequency band, k the decomposition level, and l the frequency band of the MSD representation. A and calculated by A( p) = ω( s, t) D( m+ s, n+ t, k, l) s S, t T (3) A (4) where ω (,) st, m, n, k, l are as defined in Eq.3. If M is smaller than a threshold α, ω =, and ω = 0, else if M α then And ω ω M = 2 2 α (5) = ω (6) 2.4 Region based image fusion approach To interpret our approach thoroughly, at last, we introduce our steps briefly. Coregister both images and resample the Ms image to make its pixel size equal to that of the Pan image, in order to get perfectly superposable images. Apply the PCA transform to the Ms image and obtain the PC¹ component. Generate a new Pan image whose histogram matches that of the PC¹ Apply the wavelet decomposition to the PC¹ and the corresponding histogram-matched Pan image respectively, using the Daubechies four-coefficient wavelet with decomposition level N (usually N=3 or 4), then obtain two lowfrequency subimages LL and LL¹, also 6*N high-frequency subimages. Perform the region segmentation on Ms image using edge information, the output image is a labeled image which different value represent different region, at last, resize the output image to the size of LL¹. In each corresponding region, picking M points to compute N-dimension eigenvectors V¹ and V from LL¹ and LL, to provide their difference V as the input of SVM.
4 Through SVM, we generate a classifier to classify the residual points in each region, then fusing them by certain rule described in 2.3, obtain new PC¹. Apply wavelet reconstruction and inverse PCA to obtain the fused image. 3. EXPERIMENT RESULTS In order to validate the theoretical analysis, the performance of our approach and other existing fusion techniques discussed above are further evaluated by experimentation. The IKONOS-2 chromatic band ( μm) of the - m resolution high-resolution chromatic image and the red ( μm) bands of the 4-m resolution lowresolution multispectral images were used in this experiment. The images, covering an area of the cit of sherbrooke, QC, Canada, were captured on May 20, 200. The pairs of images were geometrically registered to each other; they are display in Fig.and Fig.2. Fig.. Original chromatic image Fig.2. Original multispectral image The Fig.3 is the region labeled image. We segment Ms images using edge detection, described as [5]. From the Ms images, it can be labeled three parts, the roads and buildings, the vegetation and the maroon earth. Therefore, in Fig.3 there are three regions labeled by different color, the red indicate the roads and buildings, the blue is the maroon earth and the green is the vegetation. Based on that, we can generate three classifiers to classify the points in each region.
5 In each region, we picked 3000 points from both LL and in corresponding position of LL¹. One of third are used to train the classified model, other are tested. The result of classification is showed in Table.. From Table., the classified data of three regions are close but not equal to each other, moreover, three classifiers have good performance. Fig.3 Labeled image after segmentation In order to estimate the fused results of our proposed approach, we have used five parameters to compare with some typical fusion methods, including IHS, PCA, HPF, Brovey, wavelet. Fig.5 is consisted of subscenes of some fused result by different methods. The five parameters are correlation coefficient (CC), root mean square error (RMSE)[7], spectral angle mapper (SAM), ERGAS and Q4, and the results are displayed by Table.2 and Table.3. Fig.4 Fused result of our approach Fig.5 Comparison of the subscenes from various fusion methods
6 From Table.2, our approach shows the best experiment data except CC, this is because that CC represent the correlation coefficient between the high frequency components of the fusion product and the original Pan image, furthermore, the IHS and PCA method use the Pan image replace the I component and PC¹ directly to generate new I and PC¹, while our approach selects points from Pan image or PC¹. Obviously, the new I or PC¹ generated by IHS and PCA contains more Pan image information than that one generated by our approach. Therefore, CC of IHS and PCA method is higher than our approach. This direct replacing, however, can bring spectral distortion and loss of spatial information which discussed in section 2., and this drawback also can be shown from other parameters. Table.. Main parameters and classified performance of SVM Categories Vegetation Man-made objects Earth c g Precision 90.5% 90.8% 9.0% Recall 90.% 90.4% 90.6% Table.2. Result of evaluation parameters Brovey HIS PCA HPF Wavelet Our approach RMSE CC SAM o o o o o.5 o.2 ERGAS Table.3. Q4 for resultant images and the original multispectral image Red Green Blue NIR Average Brovey HIS PCA HPF Wavelet Our approach
7 4. CONCLUSION In this paper, we propose a novel approach to fuse chromatic image and multispectral image. The main contributions are to introduce the region based fusion approach according to different objects in remote sensing images and use SVM to combine various fusion rules as a fusion model. Region based fusion is a thoroughly considerate fusion technique; It can obtain more precise fusion results. SVM fusion rule can avoid drawbacks of single fusion rule and can be applied in various conditions. From the evaluation results, we can see that our approach can obtain better results than some typical fusion techniques, therefore, region based fusion method using SVM is good improvement to typical fusion method. 5. ACKNOWLEDGEMENTS This work was supported by Natural Science Foundation of China under Grant No REFERENCES. T.Ranchin and L.Wald, Fusion of high spatial and spectral resolution images: the arsis concept and its implementation, Photogramm.Eng.Remote Sens., vol. 66, 49-6 (2000). 2. T.A.Wilson, S.K.Rogers and M.Kabrisky, Perceptual-based image fusion for hyperspectral data, IEEE Trans.Geosci.Remote Sensing, vol.35, (997). 3. J.Nuňez, X.Otazu, O.Fors, A.Prades, V.Palà and Romàn Arbiol, Multiresolution-based image fusion with additive wavelet decomposition, IEEE Trans.Geosci.Remote Sensing, vol.37, (999). 4. María González-Audícana, José Luis Saleta, Raquel García Catalán and Rafael García, Fusion of multispectral and chromatic images using improved his and a mergers based on wavelet decomposition, IEEE Trans.Geosci.Remote Sensing, vol.42, (2004). 5. J.Scharcanski and A.N.Venetsanopoulos, Edge detection of color images using directional operators, IEEE Trans.ciruits and system for video technology, vol.7, (997). 6. Gonzalo Pajares and Manuel de la Cruz, A wavelet-based image fusion tutorial, Pattern Recognition Society, vol.37, (2004). 7. Victor J.D.Tsai, Evaluation of multiresolution image fusion algorithms. Proc. IGARSS 04, (2004).
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