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遥感数据融合是多源遥感海量数据富集表示的有效途径。如何在提高融合影像空间分辨率的同时最大限度地保持光谱信息是长期以来遥感数据融合研究的焦点内容。本文以ALOS PRISM和ALOS AVNIR-2传感器的数据为数据源,比较研究了遥感领域中常用和代表性的BROVEY、IHS、MULTIPLICATIVE、PCA、WAVELET和HPF六种融合方法,并通过主观评价和定量分析对融合效果进行了综合评价。实验结果表明,HPF方法在显著提高融合影像空间分辨率的同时,有效保持了多光谱影像的光谱信息,是适合ALOS数据的最优融合方法。
Remote sensing data fusion is an effective way to represent mass data enrichment of multi-source remote sensing. How to keep the spectral information to the maximum while improving the spatial resolution of the fusion image is the focus of the long-term research on remote sensing data fusion. In this paper, we use the data of ALOS PRISM and ALOS AVNIR-2 as the data source, and compare the six common methods of BROVEY, IHS, MULTIPLICATIVE, PCA, WAVELET and HPF in the field of remote sensing. Through subjective evaluation and quantitative analysis The fusion effect of a comprehensive evaluation. The experimental results show that the HPF method can effectively preserve the spectral information of multispectral images while significantly improving the spatial resolution of the fusion image and is an optimal fusion method suitable for ALOS data.