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Wihartini1 and Aniati Murni2 1Faculty of Engineering, Budi Luhur University Jl. Raya Ciledug, Jakarta, Indonesia Fax (62 21) 5853752, e-mail: wihartini@bl.ac.id 2Faculty of Computer Science, University of Indonesia Kampus UI, Depok 16424, Indonesia Fax (62 21) 7863415, e-mail: aniati@cs.ui.ac.id Abstract - This paper presents an information fusion method based on multiresolution and multisensor Synthetic Aperture Radar (SAR) and Optical-sensor segmented images. The result of information fusion using single resolution and multisensor segmented images gives a thematic image with artifacts. This paper has proposed an information fusion method using single or multiresolution and single or multisensor segmented images. The multiresolution segmented images are obtained using the A Troust wavelet transform and the information fusion is done using a composition approach. The results show significant improvement, such as better region boundaries of linear features more homogeneous regions of low-frequency types of object. I. Introduction The use of SAR image to obtain the thematic image of an area becomes more important due to the failure of using the optical-sensor image that contains cloud cover. In general, the thematic images produced by a classifier using either SAR or optical-sensor data have 'salt and pepper' effect. Solaiman et al. [1] has used the Nagao filter and Sun et al. [2] has used probability relaxation technique to smooth the effect of 'salt and pepper'. The à trous wavelet transformation gives an offer to filter the low-pass and high-pass components in an image into different scales [8]. In this way, the method provides a possibility to obtain the best thematic image. The wavelet-based segmentation and threshold technique provides a selection of multi-resolution segmented images that is suitable to the objects of interest. A pair of the most suitable segmented images is chosen from the SAR and optical-sensor wavelet-based segmented images and threshold technique. A reference map is used to do the task. Finally, the detail optixal-sensor segmented image is verified by the information of the detail SAR image. This paper is organized as follows. Section 1 presents the introduction and Section 2 consists of the data and methodology. The following Section 3 presents the a trous wavelet transformation that is used for filtering and segmentation and thresholding process.. Section 4 presents and discusses the experimental results. This paper is closed by a concluding remark. II. Data and Methodology 2.1 Site Area The object area of the research is Central Kalimantan, located at longitude 113053'-114000' and latitude 2008'-2011'. The vegetation in this area consists of scattered trees, rice paddies, swamps and woodlands. The data to be used is a JERS-1 SAR L-band scene (path-row: 96/304) taken on 20th March 1997 and LANDSAT TM taken on 24 March 1997. Data set is processed with standard geo-coded image re-sampled to the UTM (Universal Transverse Mercator) projection by NASDA. Map-to-map registration is used by choosing 25 GCPs (Ground Control Points), using polynomial functions with accuracy of less than 1 pixel RMS (Root Mean Square) error, and resampled with 12.5 m resolution for each pixel. 2.2 Methodology The process in this research is divided into two stages: pre-processing and intermediate processing. Pre-processing stage is doing registration and filtering, while intermediate processing stage is doing segmentation and object identification. Image registration is done by map-to-map registration from Landsat image to SAR image. Subsequently, denoising process on both images is done with à trous wavelet transformation. The segmentation process is performed for each detail image of both sensor images. The detail image is obtained by à trous wavelet transformation up to fourth level. From the resulting detail image segmentation, object identification on SAR image is performed based on the Landsat detail segmented. The whole process can be seen on Figure 1.  Figure 1. Information fusion flowchart III. À trous Wavelet Transform 3.1. À trous algorithm A wavelet transformation for discrete data can be obtained by a version known as the à trous algorithm, which means by "hole". The à trous algorithm is a non-dyadic resolution; it is a redundant or stationary transformation because there is no decimation process. Wavelet transformation with à trous 1-dimensional algorithm will produces one set {wj} on scale j, which contains the information detail and a scalar product cj that contains image approximation information. Both wj and cj has the same pixel number with the original image.  Denoising or noise reduction process is different from filtering. Filtering is done in frequency domain, that is, disposing the unwanted frequency area by thresholding where some information is disposed. But denoising is a process to dispose noise by pyramidal algorithm (Moorlet) or non-pyramidal (á trous ) algorithm. The modeled image contains two components i.e. deterministic and stochastic, where the first is a signal while the second is a noise. There are two noise models: Gaussian additive noise model and non-Gaussian multiplicative noise model. Speckle is a non-Gaussian multiplicative noise. In SAR images, the noise is multiplicative, so components are multiplied. Homomorphic is applied to separate the deterministic and stochastic components, by taking the logarithm of the image: log(z) = log(x) + log(v) ...............................(9) where : log(z) is the new image, log(x) is the signal, and log(v) is the additive noise.  
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