Curvelet based hyperspectral image fusion

Sha Wang; Hua-jun Feng; Zhi-hai Xu; Qi Li; Yue-ting Chen

doi:10.1117/12.2031476

30 August 2013 Curvelet based hyperspectral image fusion

Sha Wang, Hua-jun Feng, Zhi-hai Xu, Qi Li, Yue-ting Chen

Proceedings Volume 8910, International Symposium on Photoelectronic Detection and Imaging 2013: Imaging Spectrometer Technologies and Applications; 891005 (2013) https://doi.org/10.1117/12.2031476
Event: ISPDI 2013 - Fifth International Symposium on Photoelectronic Detection and Imaging, 2013, Beijing, China

Abstract

Hyperspectral imagery typically possesses high spectral resolution but low spatial resolution. One way to enhance the spatial resolution of a hyperspectral image is to fuse its spectral information and the spatial information of another high resolution image. In this paper, we propose a novel image fusion strategy for hyperspectral image and high spatial resolution panchromatic image, which is based on the curvelet transform. Firstly, determine a synthesized image with the specified RGB bands of the original hyperspectral images according to the optimal index factor (OIF) model. Then use the IHS transform to extract the intensity component of the synthesized image. After that, the histogram matching is performed between the intensity component and the panchromatic image. Thirdly, the curvelet transform is applied to decompose the two source images (the intensity component and the panchromatic image) in different scales and directions. Different fusion strategies are applied to coefficients in various scales and directions. Finally, the fused image is achieved by the inverse IHS transform. The experimental result shows that the proposed method has a superior performance. Comparing with the traditional methods such as the PCA transform, wavelet-based or pyramid-based methods and the multi-resolution fusion methods (shearlet or contourlet decomposition), the fused image achieves the highest entropy index and average gradient value. While providing a better human visual quality, a good correlation coefficient index indicates that the fused image keeps good spectral information. Both visual quality and objective evaluation criteria demonstrate that this method can well preserve the spatial quality and the spectral characteristics.

Citation Download Citation

Sha Wang, Hua-jun Feng, Zhi-hai Xu, Qi Li, and Yue-ting Chen "Curvelet based hyperspectral image fusion", Proc. SPIE 8910, International Symposium on Photoelectronic Detection and Imaging 2013: Imaging Spectrometer Technologies and Applications, 891005 (30 August 2013); https://doi.org/10.1117/12.2031476

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KEYWORDS

Image fusion

Hyperspectral imaging

Image quality

RGB color model

Spatial resolution

Image processing

Principal component analysis

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