THE OPTIMIZED BLOCK-REGRESSION-BASED FUSION ALGORITHM FOR PAN SHARPENING OF VERY HIGH RESOLUTION SATELLITE IMAGERY

被引：0
|
作者：
Zhang, J. X. [1 ]
Yang, J. H. [1 ,2 ]
Reinartz, P. [2 ]
机构：
[1] CASM, Beijing 100830, Peoples R China
[2] German Aerosp Ctr DLR, Remote Sensing Technol Inst, D-82234 Wessling, Germany
来源：
XXIII ISPRS CONGRESS, COMMISSION VII | 2016年 / 41卷 / B7期
关键词：
Remote sensing; Satellite imagery; Very high resolution; Image fusion; REMOTE-SENSING IMAGES; PANSHARPENING ALGORITHMS; TRANSFORM METHOD;
D O I：
10.5194/isprsarchives-XLI-B7-739-2016
中图分类号：
P9 [自然地理学];
学科分类号：
0705 ; 070501 ;
摘要：
Pan-sharpening of very high resolution remotely sensed imagery need enhancing spatial details while preserving spectral characteristics, and adjusting the sharpened results to realize the different emphases between the two abilities. In order to meet the requirements, this paper is aimed at providing an innovative solution. The block-regression-based algorithm (BR), which was previously presented for fusion of SAR and optical imagery, is firstly applied to sharpen the very high resolution satellite imagery, and the important parameter for adjustment of fusion result, i.e., block size, is optimized according to the two experiments for Worldview-2 and QuickBird datasets in which the optimal block size is selected through the quantitative comparison of the fusion results of different block sizes. Compared to five fusion algorithms (i.e., PC, CN, AWT, Ehlers, BDF) in fusion effects by means of quantitative analysis, BR is reliable for different data sources and can maximize enhancement of spatial details at the expense of a minimum spectral distortion.
引用
收藏
页码：739 / 746
页数：8
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