提出了一种基于分类的融合算法 ,可用于融合低分辨率多光谱影像和配准的高分辨率全色波段影像 .算法的主要步骤如下 :(1)将 1m高分辨率全色波段影像和 4 m低分辨率多光谱影像进行几何配准 ;(2 )采用监督或非监督分类算法对高分辨率影像和配准的多光谱影像进行统一分类 ;(3)根据每一类所对应的高分辨率全色波段影像直方图和相应的空间关系 ,对配准后单个波段的多光谱影像进行调整 .(4)采用柱状坐标系对调整后的多谱影像进行 HIS(Hue,Intensity,Saturation)变换 ,并反变换至 RGB(red,green,blue)彩色空间 ,从而得到融合影像 .以天安门附近 10 0× 10 0大小 IKONOS的 1m高分辨率全色波段影像和 4 m多光谱影像为例 ,对融合算法进行了验证 .实验结果表明 :(1)此算法可以融合分类信息、全色波段的高分辨率信息和多光谱波段的光谱信息 ,突出分类信息作为先验知识的重要性 [1 ] ;(2 )在精确分类的基础上 ,可部分消除目标物边界的假彩色现象 ,有较好的目视判读效果 ;(3)对融合过程中 ,先验知识与空间关系的加入作了一些有益的尝试 .

This paper presents a new classification-based fusion scheme, which is used throughout this paper as a synonym of the "pixel-level fusion". It can be applied to merge low-resolution images and co-registered high-resolution images. The key of this trial is as follows: (1) Geometrical co-registration IKONOS 1-m panchromatic image and 4-m multi-spectral image; (2) Classification of the High-resolution Panchromatic image together with the low-resolution images using supervised or unsupervised algorithms; (3) According to each class histogram of High-resolution image, adjustment of the corresponding spectral values of single multi-spectral image; (4) HIS transformation of the adjusted multi-spectral image using cylindrical coordinates, and acquirement of the fusion images. From this algorithm, we can conclude as follows: (1) The proposed method can merge the class information, spatial information of high resolution image and spectral information of Low-resolution image, accounting for the importance of classification as prior knowledge ; (2) Based on precise classification, it can effectively eliminate the false color at the edge of objective and have better visual effects; (3) It can try to add classification and spatial relationship to image fusion. In this trial the selected Peking image of IKONOS will be used, which can bring us convenience when verifying our algorithm.