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A NOVEL IHS-GA FUSION METHOD BASED ON ENHANCEMENT VEGETATED AREA

机译:基于增强植被区的新型IHS-GA融合方法

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Pan sharpening methods aim to produce a more informative image containing the positive aspects of both source images. However, the pan sharpening process usually introduces some spectral and spatial distortions in the resulting fused image. The amount of these distortions varies highly depending on the pan sharpening technique as well as the type of data. Among the existing pan sharpening methods, the Intensity-Hue-Saturation (IHS) technique is the most widely used for its efficiency and high spatial resolution. When the IHS method is used for IKONOS or QuickBird imagery, there is a significant color distortion which is mainly due to the wavelengths range of the panchromatic image. Regarding the fact that in the green vegetated regions panchromatic gray values are much larger than the gray values of intensity image. A novel method is proposed which spatially adjusts the intensity image in vegetated areas. To do so the normalized difference vegetation index (NDVI) is used to identify vegetation areas where the green band is enhanced according to the red and NIR bands. In this way an intensity image is obtained in which the gray values are comparable to the panchromatic image. Beside the genetic optimization algorithm is used to find the optimum weight parameters in order to gain the best intensity image. Visual and statistical analysis proved the efficiency of the proposed method as it significantly improved the fusion quality in comparison to conventional IHS technique. The accuracy of the proposed pan sharpening technique was also evaluated in terms of different spatial and spectral metrics. In this study, 7 metrics (Correlation Coefficient, ERGAS, RASE, RMSE, SAM, SID and Spatial Coefficient) have been used in order to determine the quality of the pan-sharpened images. Experiments were conducted on two different data sets obtained by two different imaging sensors, IKONOS and QuickBird. The result of this showed that the evaluation metrics are more promising for our fused image in comparison to other pan sharpening methods.
机译:PAN锐化方法旨在产生更具信息丰富的图像,包含两个源图像的正面。然而,PAN锐化过程通常在所得到的融合图像中引入一些光谱和空间扭曲。这些扭曲的数量根据PAN锐化技术以及数据类型而变化很大。在现有的PAN锐化方法中,强度 - 色调饱和度(IHS)技术是最广泛应用于其效率和高空间分辨率。当IHS方法用于IKONOS或Quickbird图像时,存在显着的颜色变形,主要是由于平面图像的波长范围。关于绿色植被区的事实,全色灰度值远大于强度图像的灰度值。提出了一种新的方法,其在空间上调整植被区域中的强度图像。为此,归一化差异植被指数(NDVI)用于识别根据红色和NIR条带增强了绿色带的植被区域。以这种方式获得强度图像,其中灰度值与平面图像相当。除了遗传优化算法旁边,用于找到最佳权重参数,以获得最佳的强度图像。视觉和统计分析证明了拟议方法的效率,因为与常规IHS技术相比,它显着提高了融合质量。在不同的空间和光谱度量方面还评估了所提出的PAN锐化技术的准确性。在本研究中,已经使用了7个度量(相关系数,ergas,rase,RMSE,SAM,SID和空间系数)以确定泛尖图像的质量。在由两个不同的成像传感器,Ikonos和Quickbird获得的两组上进行实验。结果表明,与其他PAN锐化方法相比,评估度量对我们的融合图像更加了解。

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