A Regression-Based High-Pass Modulation Pansharpening Approach

被引:98
|
作者
Vivone, Gemine [1 ]
Restaino, Rocco [1 ]
Chanussot, Jocelyn [2 ,3 ]
机构
[1] Univ Salerno, Dept Informat Engn Elect Engn & Appl Math, I-84084 Fisciano, Italy
[2] Univ Grenoble Alpes, GIPSA Lab, Grenoble INP, CNRS, F-38000 Grenoble, France
[3] Univ Iceland, Fac Elect & Comp Engn, IS-107 Reykjavik, Iceland
来源
关键词
Data fusion; high-pass modulation (HPM); least squares estimation; linear regression model; multiresolution analysis (MRA); pansharpening; remote sensing; SPECTRAL RESOLUTION IMAGES; PAN-SHARPENING METHOD; MULTISPECTRAL IMAGES; DATA-FUSION; WAVELET; MULTIRESOLUTION; REPRESENTATION; ALGORITHMS; DECOMPOSITION; SEGMENTATION;
D O I
10.1109/TGRS.2017.2757508
中图分类号
P3 [地球物理学]; P59 [地球化学];
学科分类号
0708 ; 070902 ;
摘要
Pansharpening usually refers to the fusion of a high spatial resolution panchromatic (PAN) image with a higher spectral resolution but coarser spatial resolution multispectral (MS) image. Owing to the wide applicability of related products, the literature has been populated by many papers proposing several approaches and studies about this issue. Many solutions require a preliminary spectral matching phase wherein the PAN image is matched with the MS bands. In this paper, we propose and properly justify a new approach for performing this step, demonstrating that it yields state-of-the-art performance. The comparison with existing spectral matching procedures is performed by employing four data sets, concerning different kinds of landscapes, acquired by the Pleiades, WorldView-2, and GeoEye-1 sensors.
引用
收藏
页码:984 / 996
页数:13
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