混叠
多光谱图像
计算机科学
全色胶片
重采样
消除混叠
人工智能
图像质量
投影(关系代数)
采样(信号处理)
模式识别(心理学)
图像融合
光传递函数
计算机视觉
比例(比率)
算法
图像(数学)
滤波器(信号处理)
物理
光学
语音识别
欠采样
音频信号处理
音频信号
语音编码
量子力学
作者
Stefano Baronti,Bruno Aiazzi,Massimo Selva,Andrea Garzelli,Luciano Alparone
出处
期刊:IEEE Journal of Selected Topics in Signal Processing
[Institute of Electrical and Electronics Engineers]
日期:2011-01-10
卷期号:5 (3): 446-453
被引量:131
标识
DOI:10.1109/jstsp.2011.2104938
摘要
In this paper, the characteristics of multispectral (MS) and panchromatic (P) image fusion methods are investigated. Depending on the way spatial details are extracted from P, pansharpening methods can be broadly labeled into two main classes, corresponding to methods based on either component substitution (CS) or multiresolution analysis (MRA). Theoretical investigations and experimental results evidence that CS-based fusion is far less sensitive than MRA-based fusion to: 1) registration errors, i.e., spatial misalignments between MS and P images, possibly originated by cartographic projection and resampling of individual data sets; 2) aliasing occurring in MS bands and stemming from modulation transfer functions (MTF) of MS channels that are excessively broad for the sampling step. In order to assess the sensitiveness of methods, aliasing is simulated at degraded spatial scale by means of several MTF-shaped digital filters. Analogously, simulated misalignments, carried out at both full and degraded scale, evidence the quality-shift tradeoff of the two classes. MRA yields a slightly superior quality in the absence of aliasing/misalignments, but is more penalized than CS, whenever either aliasing or shifts between MS and P occur. Conversely, CS generally produces a slightly lower quality, but is intrinsically more aliasing/shift tolerant.
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