Share Email Print

Optical Engineering

Adaptive steepest descent method for pan-sharpening of multispectral images
Author(s): Lining Liu; Yunhong Wang; Yiding Wang
Format Member Price Non-Member Price
PDF $20.00 $25.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper 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.

Paper Details

Date Published: 1 September 2011
PDF: 13 pages
Opt. Eng. 50(9) 097005 doi: 10.1117/1.3622484
Published in: Optical Engineering Volume 50, Issue 9
Show Author Affiliations
Lining Liu, BeiHang Univ. (China)
Yunhong Wang, BeiHang Univ. (China)
Yiding Wang, North China Univ. of Technology (China)

© SPIE. Terms of Use
Back to Top