Adaptive steepest descent method for pan-sharpening of multispectral images

Lining Liu; Yunhong Wang; Yiding Wang

doi:10.1117/1.3622484

1 September 2011 Adaptive steepest descent method for pan-sharpening of multispectral images

Lining Liu, Yunhong Wang, Yiding Wang

Author Affiliations +

Optical Engineering, Vol. 50, Issue 9, 097005 (September 2011). https://doi.org/10.1117/1.3622484

Abstract

Many pan-sharpening techniques have been developed to synthesize a multispectral (MS) image at high resolution by fusing MS images and panchromatic (Pan) images. Most existing pan-sharpening methods can achieve results with high spatial resolution, but the spectral distortion in the fused results is still a problem that needs to be solved. In this paper, an adaptive linear model is proposed to reduce the spectral distortion by weakening the dependence on the correlation between Pan and MS. The difference between a Pan image and the combination of MS images is estimated by least square optimization, and embedded into the proposed model as a virtual band. According to the adaptive model, an iterative pan-sharpening algorithm is proposed based on the steepest descent method, in which the virtual band is used as a local adaptive constraint to the optimized solution. The proposed method is tested on datasets acquired by IKONOS, QuickBird, and Landsat 7 ETM+ and compared with the existing methods. The quality measures and the visual impressions show that the proposed method is an efficient approach to preserving spectral information and represents strong robustness against various scenes and sensors.

©(2011) Society of Photo-Optical Instrumentation Engineers (SPIE)

Citation Download Citation

Lining Liu, Yunhong Wang, and Yiding Wang "Adaptive steepest descent method for pan-sharpening of multispectral images," Optical Engineering 50(9), 097005 (1 September 2011). https://doi.org/10.1117/1.3622484

Published: 1 September 2011

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