Hybrid fusion using Gram Schmidt and Curvelet transforms for satellite images. Issue 1 (August 2021)

• Record Type: Journal Article
• Authors: Hammad, M; Ghoniemy, T; Mahmoud, T; Amein, A
• Is Part Of: IOP conference series. Volume 1172:Issue 1(2021)
• Publication Date: 2021-08
• Subjects: Materials science -- Periodicals; 620.1105
• DOI: 10.1088/1757-899X/1172/1/012016
• Languages: English
• ISSNs: 1757-8981

Abstract

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 pre-processing 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 benchmark-datasets. Consequently, the spectral and spatial qualities of the fused images are assessed subjectively as well as objectively using different quality metrics. Moreover, the proposed method is compared with state-of-the-art fusion techniques and results show the robustness of the proposed method that has the best result in spatial and spectral evaluation metrics such as, Quality with No Reference (QNR), Peak Signal to Noise Ratio (PSNR), Standard Deviation (SD), Entropy (ENT) and Spectral Correlation Coefficient (SCC) metrics.