| Hybrid Fusion using Gram Schmidt and Curvelet Transforms for Satellite Images |
| Paper ID : 1048-ASAT19-FULL (R2) |
| Authors: |
|
Mahmoud Mohamed Hammad *1, Tarek Ghoniemy1, Tarek Mahmoud1, Ahmed Saleh Amein2 1Electrical Engineering, Military Technical College, Cairo, Egypt 2Electrical Engineering, Senior IEEE, FISCS, October 6 University, Cairo, Egypt |
| Abstract: |
| Optical satellites generally provide high-resolution panchromatic but low-resolution multispectral images which provide structural details of features and spectral information respectively. Nowadays, fusion of the two types of resolutions, to have complementary information, becomes increasingly essential for many applications such as microscopic, astronomical and satellite imagery. In this paper, a novel hybrid pixel-level image fusion method, is proposed for benefiting from both panchromatic (PAN) and multispectral (MUL) images. The proposed method integrates Gram Schmidt (GS) and curvelet transforms (CVT), by the aid of local energy and maximum fusion rules, for reducing individual method limitations and achieving both better spectral consistency and spatial details preservation. After a preprocessing stage, orthonormal bases are obtained for low spatial resolution images by using GS transform. Then, high-resolution and low-resolution images are fused using CVT by the aid of histogram matching. Finally, the fused image is obtained by applying both curvelet and GS inverse transforms. The performance of the proposed method is evaluated using publicly available Pleiades datasets. Consequently, the spectral and spatial qualities of the fused images are assessed not only subjectively, but also, objectively using different quality metrics. Moreover, the proposed method is compared with component substitution and multi-resolution analysis state-of-the-art fusion techniques and results show the robustness of the proposed method. |
| Keywords: |
| Gram Schmidt, Curvelet transform, Pixel-level fusion, Hybrid image fusion. |
| Status : Paper Accepted (Oral Presentation) |
