A novel remote-sensing image fusion method based on hybrid visual saliency analysis
A novel remote-sensing image fusion method based on hybrid visual saliency analysis
复制标题
一种基于混合视觉显着性分析的遥感图像融合新方法
DOI：
10.1080/01431161.2018.1479791
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发表时间：
2018
影响因子：
3.4
通讯作者：
Zhang Jue
中科院分区：
文献类型：
--
作者：
Zhang Libao;Zhang Jue
在实际应用中，图像中不同区域对空间分辨率和光谱分辨率的要求实际上是不同的。然而，现有的大多数方法都是对整个图像进行统一的融合处理，而没有考虑到这种多样化的需求。针对这一问题，提出了一种基于显著性分析的遥感图像融合新方法。通过引入混合视觉显著性分析，将全色图像和多光谱图像中的区域自动划分为显著区和非显著区。然后，采用子区域融合策略分别融合非显著和显著区域；对于居住区和道路等突出区域，采用自适应强度-色调-饱和度（adaptive intensity-hue-saturation，自适应IHS）方法，可有效改善空间质量。对于农田、森林、草原等非显著区域，在空间细节提取过程中采用双树复小波变换，并对自适应IHS方法得到的组合系数进行综合，抑制光谱畸变。实验结果表明，我们的方案提供了最先进的性能，并在不同区域的空间注入和光谱保持之间取得了更好的平衡。
In practical applications, different regions in images may practically have different demands for the spatial and spectral resolution. However, most existing methods execute a unified fusion processing of the whole image with no consideration of such diverse demands. In this article, a new fusion method for remote-sensing images based on saliency analysis is proposed for addressing the issue. By introducing the hybrid visual saliency analysis, regions in the panchromatic (Pan) image and multispectral image would be automatically partitioned into two parts: salient and non-salient regions. Then, a subregion fusion strategy is applied to fuse the non-salient and salient regions, respectively. As for salient regions such as residential areas and roads, the adaptive intensity–hue–saturation (adaptive IHS) method is implemented for its merit of effective improvement in spatial quality. For non-salient regions such as farmland, forest, and grassland, the dual-tree complex wavelet transform is used in the process of spatial detail extraction, and the combination coefficients yielded by the adaptive IHS method are integrated to suppress the spectral distortion. Experimental results demonstrate that our proposal provides state-of-the-art performance as well as achieves a better balance between spatial injection and spectral maintenance in different regions.