Forest classification on the basis of satellite Images is a promising technique both for primary map production and for map updating and forest monitoring. For accurate forest classification into three classes, using mapping by canopy cover density “high spatial resolution satellite Images have to be used in order to obtain the required spatial detail” [Schneider, 1999]. At the same time, the spectral information necessary for identifying certain class types can most economically be derived from multi-spectral Images of medium spatial resolution. fusion techniques have to be used to combine information from both sources. In this paper, a method was developed for object-level fusion of IRS-1C/1D pan Images (5.8 m pixel size) and LANDSAT TM multispectral Images (30 m pixel size) and subsequent classification to produce a canopy cover classification of the northern forests of Iran. The study area is located in Sari and its forest regions in 60,000Hec. (Figure 1) The individual processing steps included segmentation of a multi-band Image consisting of both the high-spatial-resolution pan Image band and medium-spatial-resolution multispectral bands, with proper weighting of the individual bands in the segmentation procedure in order to obtain both fine detail from the pan Image and coarser boundary delineations which show up only in multispectral Images. For classification, fuzzy logic membership functions were used. Verification of the classification was carried out and checked with error matrix and kappa calculation on a selected transect from a newly classified map. The results showed that employing object-based fusion procedure using medium- and high-resolution data was an appropriate method that improved classification. Comparing the hard work of creating a new topographic map, a pixel-based fusion procedure was demonstrated to be an acceptable method to create a satellite Image map (Satmap) for visual monitoring activities and programs. The overall accuracy of the map produced was calculated as a topo-map of the region.

Background: Medical Image fusion is being widely used for capturing complimentary information from Images of different modalities. Combination of useful information presented in medical Images is the aim of Image fusion techniques, and the fused Image will exhibit more information in comparison with source Images. Objective: In the current study, a BEMD-based multi-modal medical Image fusion technique is utilized. Moreover, Teager-Kaiser energy operator (TKEO) was applied to lower BIMFs. The results were compared to six routine methods. Material and Methods: In this study, which is of experimental type, an Image fusion technique using bi-dimensional empirical mode decomposition (BEMD), Teager-Kaiser energy operator (TKEO) as a local feature selection and Hierarchical Model And X (HMAX) model is presented. BEMD fusion technique can preserve much functional information. In the process of fusion, we adopt the fusion rule of TKEO for lower bi-dimensional intrinsic mode functions (BIMFs) of two Images and HMAX visual cortex model as a fusion rule for higher BIMFs, which are verified to be more appropriate for human vision system. Integrating BEMD and this efficient fusion scheme can retain more spatial and functional features of input Images. Results: We compared our method with IHS, DWT, LWT, PCA, NSCT and SIST methods. The simulation results and fusion performance show that the presented method is effective in terms of mutual information, quality of fused Image (QAB/F), standard deviation, peak signal to noise ratio, structural similarity and considerably better results compared to six typical fusion methods. Conclusion: The statistical analyses revealed that our algorithm significantly improved spatial features and diminished the color distortion compared to other fusion techniques. The proposed approach can be used for routine practice. fusion of functional and morphological medical Images is possible before, during and after treatment of tumors in different organs. Image fusion can enable interventional events and can be further assessed.

The fusion of medical Images is very useful for clinical application. Generally, the PET Image indicates the function of tissue and the MRI Image shows the anatomy of tissue. In this article we fused MRI and PET Images and the purpose is adding structural information from MRI to PET Image. The Images decomposed with Curvelet Transform, and then two Images fused with applying fusion rules. We used MATLAB software for fused Images and evaluated the result.  The data set consists 34 Images of color PET Images and high resolution MRI Images. The brain Images are classified into two groups, normal (Coronal, Sagittal and Transaxial) and Alzeimer’s disease dataset Images. Finally we used visual and quantitative criteria to evaluate the fusion result. In quantitative evaluation we used entropy, discrepancy and overall performance. Results show the amount of entropy, achieved by the proposed method, was the highest and amount of discrepancy and overall performance was the lowest. The small amount of discrepancy, overall performance and high amount of entropy means high quality.