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首页> 外文期刊>American journal of applied sciences >Application of Morphological Component Analysis to Optical Image Fusion
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Application of Morphological Component Analysis to Optical Image Fusion

机译：形态成分分析在光学图像融合中的应用

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摘要

The image fusion technique is widely used in remote sensing. Its purpose is to provide comprehensive information without arte facts by combining the partial information from different source images. In this study, we propose a new model of images fusion with very high spatial resolution. We use the separation capacities of the Morphological Component Analysis (MCA) to extract the smooth and texture components of our images. These morphological components are then fused separately using the decomposition in the Laplacian pyramids for the smooth part and bivariate Hahn polynomials for texture part. Finally the image fusion is obtained through linear combination of merged smooth and texture components. The experiments carried out on IKONOS, LANDSAT and Quick Bird remote sensing images show the good performances of our method which has been compared to conventional methods. The performances obtained in our experiments are characterized by a small global metric such as ERGAS equals to 3.88 for IKONOS image and 3.65 for QuickBird image compared to 8.70 for IKONOS image and 6.97 for QuickBird for conventional HIS algorithms. We also have a mean loss of 15% for spectral information com pare to those of the conventional methods which revolve around 25%. The degradation of spatial information in order of 17% in contrast to conventional HIS algorithms which oscillate around 21%. 
机译：图像融合技术被广泛地用于遥感中。其目的是通过组合来自不同源图像的部分信息来提供无事实依据的全面信息。在这项研究中，我们提出了一种具有很高空间分辨率的图像融合新模型。我们使用形态成分分析（MCA）的分离能力来提取图像的平滑成分和纹理成分。然后，使用拉普拉斯金字塔中的分解将平滑部分和二元Hahn多项式分解作为纹理部分，将这些形态成分分别融合。最后，通过融合平滑分量和纹理分量的线性组合获得图像融合。在IKONOS，LANDSAT和Quick Bird遥感影像上进行的实验表明，与传统方法相比，我们的方法具有良好的性能。在我们的实验中获得的性能具有较小的整体度量标准，例如，ERGAS对于IKONOS图像等于3.88，对于QuickBird图像等于3.65，而对于IKONOS图像则为8.70，对于传统HIS算法为6.97。我们的频谱信息平均损失为15％，而传统方法大约为25％。与传统的HIS算法（约21％的振荡）相比，空间信息的退化约为17％。

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