Geostatistical solutions for super-resolution land cover mapping

被引:86
|
作者
Boucher, Alexandre [1 ]
Kyriakidis, Phaedon C. [2 ]
Cronkite-Ratcliff, Collin [1 ]
机构
[1] Stanford Univ, Dept Geol & Environm Sci, Stanford, CA 94305 USA
[2] Univ Calif Santa Barbara, Dept Geog, Santa Barbara, CA 93106 USA
来源
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING | 2008年 / 46卷 / 01期
基金
美国国家科学基金会;
关键词
geostatistics; spatial uncertainty; subpixel mapping;
D O I
10.1109/TGRS.2007.907102
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
Super-resolution land cover mapping aims at producing fine spatial resolution maps of land cover classes from a set of coarse-resolution class fractions derived from satellite information via, for example, spectral unmixing procedures. Based on a prior model of spatial structure or texture that encodes the expected patterns of classes at the fine (target) resolution, this paper presents a sequential simulation framework for generating alternative super-resolution maps of class labels that are consistent with the coarse class fractions. Two modes of encapsulating the prior structural information are investigated one uses a set of indicator variogram models, and the other uses training images. A case study illustrates that both approaches lead to super-resolution class maps that exhibit a variety of spatial patterns ranging from simple to complex. Using four different examples, it is demonstrated that the structural model controls the patterns seen on the super-resolution maps, even for cases where the coarse fraction data are highly constraining.
引用
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页码:272 / 283
页数:12
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