Radiometric Enhancement of Landsat 8 OLI Imagery Using Coastal/Aerosol Band

Syam’ani
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Abstract

The presence of atmospheric particles in multispectral imageries such as Landsat 8 OLI can reduce the visual acuity of the imageries. The most ideal method to reduce the existence of atmospheric particles in the imagery, as well as to enhance the visual appearance of the imagery, is to employ atmospheric corrections. However, atmospheric corrections are a very complex process. Besides, sometimes the results don't have an impact visually. There are many other methods to enhance imagery radiometrically, either by stretching the pixel value, shifting the histogram, or reducing the presence of clouds. This research aims to develop practical formulations to enhance the spectral value of the Landsat 8 OLI imagery bands, by reducing the presence of aerosol particles using the C/A band. Several regression models were involved in the construction process of these formulations. The accuracy assessment was performed using the Pearson correlation coefficient and RMSE, using the USGS Landsat 8 OLI TOC imagery as a comparison. The results showed that the radiometric imagery enhancement using the C/A band gave satisfactory results. Apart from providing a significant visual sharpness increase, for the exponential model with parameters, the average Pearson correlation coefficient is 0.96, with an RMSE value of 0.04, relative to the USGS Landsat 8 OLI TOC product. For a more practical model, we can omit the parameters in the exponential model. The results that will be obtained are still quite accurate. Furthermore, we can implement this enhancement model directly on digital numbers.
海岸带/气溶胶波段对Landsat 8 OLI影像的辐射增强
在诸如Landsat 8 OLI这样的多光谱图像中,大气颗粒的存在会降低图像的视觉灵敏度。减少图像中大气颗粒的存在以及增强图像视觉外观的最理想方法是使用大气校正。然而,大气校正是一个非常复杂的过程。此外,有时结果在视觉上没有影响。还有许多其他方法可以通过扩展像素值、移动直方图或减少云的存在来增强图像的辐射。本研究旨在开发实用的配方,通过使用C/A波段减少气溶胶颗粒的存在,来提高Landsat 8 OLI图像波段的光谱值。在这些公式的构建过程中涉及了几个回归模型。使用Pearson相关系数和RMSE进行精度评估,并使用USGS Landsat 8 OLI TOC图像作为比较。结果表明,采用C/A波段进行辐射图像增强,效果满意。除了提供显著的视觉清晰度增加外,对于带参数的指数模型,相对于USGS Landsat 8 OLI TOC产品,平均Pearson相关系数为0.96,RMSE值为0.04。对于更实用的模型,我们可以省略指数模型中的参数。得到的结果仍然是相当准确的。此外,我们可以直接在数字上实现该增强模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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