泛锐化方法在WorldView-2图像融合中的应用评估

Hui Li, L. Jing, Yunwei Tang, Qingjie Liu, H. Ding, Zhongchang Sun, Yu Chen
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引用次数: 5

摘要

开发了各种多光谱(MS)和全色(PAN)融合(或泛锐化)算法来生成高空间分辨率的增强MS图像。考虑到WV-2图像在PAN和MS波段上的新颖性,本研究的目的是利用图像质量指标和用于城市信息提取的信息指标,评估9种最先进的WV-2图像泛锐化方法的性能。RASE、ERGAS、SAM和Q4 4个质量指标的比较表明,HR法对WV-2 MS和PAN图像的处理效果最好。然而,四种信息指标的比较表明,数据级别的质量越高,并不意味着物体识别的信息保存越好。
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Assessment of pan-sharpening methods applied to WorldView-2 image fusion
Various multispectral (MS) and panchromatic (PAN) fusion (or pan-sharpening) algorithms were developed to produce an enhanced MS image of high spatial resolution. Regarding the novelty in both the PAN and MS bands of the WV-2 imagery, the objective of this study is to assess the performance of nine state-of-the-art pan-sharpening methods for the WV-2 imagery, using both image quality indices and information indices that used for urban information extraction. The comparison of the four quality indices (RASE, ERGAS, SAM, and Q4) demonstrated that the HR method performed the best for the WV-2 MS and PAN images. However, the comparison of the four information indices showed that a higher quality at data level does not signify better information preservation for object recognition.
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