哥伦比亚安第斯山脉大气监测的数字图像处理

IF 1.8 Q3 REMOTE SENSING
Yhesly López, E. Pawelko, Daniel Nisperuza
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引用次数: 0

摘要

摘要作为当前技术的替代方案,我们探索了使用低成本和大规模使用数码相机作为光度传感器来检索哥伦比亚安第斯山脉城市区域大气总光学深度(τ)的可行性。这项研究提出了一种简单的方法,根据比尔-布格-兰伯特定律-兰利线性拟合,从太阳图像的数字处理中估计τ,该线性拟合用于相机传感器像素配准的红色、绿色和蓝色通道的颜色水平。2022年2月至3月,从图像中检索到的τ值与从太阳光谱辐射计(SSR)检索到的值相关联。我们发现τ对当地大气的特征变化和相机的曝光参数设置是敏感的。在部分晴朗的天空条件下,大约80%(r > 0.8)的τ值与SSR系统的τ值呈线性对应关系。它的光谱依赖性(τ_red<τ_green<τ_blue)符合光-大气相互作用中的物理现象。结果表明,所应用的方法可用于监测世界上任何地理位置的大气。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Digital image processing for atmospheric monitoring at Colombian Andes
ABSTRACT As an alternative to the current technologies, we explored the feasibility of using low cost and massive use of digital cameras as photometric sensors to retrieve the atmospheric total optical depth (τ) in the urban area of a city in the Colombian Andes. This study proposes a simple way to estimate τ from digital processing of images of the Sun based on the Beer-Bouguer-Lambert law Langley’s linear fitting for the colour levels in channels red, green, and blue registered by the pixels of cameras’ sensors. From February to March 2022, the τ values retrieved from the images were correlated to the retrieved values from a solar spectral radiometer (SSR). We found that τ is sensible to the featured changes in the local atmosphere and to the cameras’ exposure parameters setup. Under conditions of partly clear sky, around 80% (r > 0.8) of the τ values from cameras showed a linear correspondence to those retrieved from SSR system. Its spectral dependency (τ _red < τ _green < τ _blue) is in accordance with the physical phenomena in light-atmosphere interaction. The results suggest that the methodology applied can be used for monitoring the atmosphere at any geographical location in the world.
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来源期刊
CiteScore
5.00
自引率
0.00%
发文量
10
期刊介绍: International Journal of Image and Data Fusion provides a single source of information for all aspects of image and data fusion methodologies, developments, techniques and applications. Image and data fusion techniques are important for combining the many sources of satellite, airborne and ground based imaging systems, and integrating these with other related data sets for enhanced information extraction and decision making. Image and data fusion aims at the integration of multi-sensor, multi-temporal, multi-resolution and multi-platform image data, together with geospatial data, GIS, in-situ, and other statistical data sets for improved information extraction, as well as to increase the reliability of the information. This leads to more accurate information that provides for robust operational performance, i.e. increased confidence, reduced ambiguity and improved classification enabling evidence based management. The journal welcomes original research papers, review papers, shorter letters, technical articles, book reviews and conference reports in all areas of image and data fusion including, but not limited to, the following aspects and topics: • Automatic registration/geometric aspects of fusing images with different spatial, spectral, temporal resolutions; phase information; or acquired in different modes • Pixel, feature and decision level fusion algorithms and methodologies • Data Assimilation: fusing data with models • Multi-source classification and information extraction • Integration of satellite, airborne and terrestrial sensor systems • Fusing temporal data sets for change detection studies (e.g. for Land Cover/Land Use Change studies) • Image and data mining from multi-platform, multi-source, multi-scale, multi-temporal data sets (e.g. geometric information, topological information, statistical information, etc.).
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