缩小尺度
计算机科学
克里金
图像分辨率
图像融合
连贯性(哲学赌博策略)
算法
图像(数学)
遥感
数据挖掘
人工智能
模式识别(心理学)
数学
机器学习
地质学
统计
海洋学
气候变化
作者
Qunming Wang,Wenzhong Shi,Peter M. Atkinson
出处
期刊:IEEE Transactions on Geoscience and Remote Sensing
[Institute of Electrical and Electronics Engineers]
日期:2020-01-01
卷期号:58 (1): 45-57
被引量:10
标识
DOI:10.1109/tgrs.2019.2930764
摘要
Spatial downscaling is an ill-posed, inverse problem, and information loss (IL) inevitably exists in the predictions produced by any downscaling technique. The recently popularized area-to-point kriging (ATPK)-based downscaling approach can account for the size of support and the point spread function (PSF) of the sensor, and moreover, it has the appealing advantage of the perfect coherence property. In this article, based on the advantages of ATPK and the conceptualization of IL, an IL-guided image fusion (ILGIF) approach is proposed. ILGIF uses the fine spatial resolution images acquired in other wavelengths to predict the IL in ATPK predictions based on the geographically weighted regression (GWR) model, which accounts for the spatial variation in land cover. ILGIF inherits all the advantages of ATPK, and its prediction has perfect coherence with the original coarse spatial resolution data which can be demonstrated mathematically. ILGIF was validated using two data sets and was shown in each case to predict downscaled images more accurately than the compared benchmark methods.
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