Classification and change detection using Landsat TM data: When and how to correct atmospheric effects?
Classification and change detection using Landsat TM data: When and how to correct atmospheric effects?
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DOI：
10.1016/s0034-4257(00)00169-3
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发表时间：
2001-02-01
影响因子：
13.5
通讯作者：
Macomber, SA
中科院分区：
文献类型：
--
作者：
Song, C;Woodcock, CE;Macomber, SA
卫星在太阳光谱中收集的电磁辐射 (EMR) 信号在从地球表面穿过大气层到达传感器时，会因气体和气溶胶的散射和吸收而发生变化。何时以及如何校正大气影响取决于可用的遥感和大气数据、所需的信息以及用于提取信息的分析方法。在许多涉及分类和变化检测的应用中，只要训练数据和待分类数据处于相同的相对尺度，则不需要大气校正。在其他情况下，校正必须将多时相数据放在相同的辐射测量尺度上，以便随着时间的推移监测陆地表面。使用由 1988 年至 1996 年中国广东省珠江三角洲的 7 张 Land-sat 5 Thematic Mapper (TM) 图像组成的多时相数据集，将缝合的绝对大气校正算法和一种相对大气校正算法与未校正的原始数据进行比较。基于分类和变化检测结果，所有修正都改进了数据分析。使用一种新方法获得了最佳的整体结果，该方法将瑞利散射的效果添加到传统的暗物体减除中。尽管此方法可能无法获得准确的表面反射率，但它可以最大程度地减少土地覆盖类别内随时间变化的反射率差异（使用 Jeffries-Matusita 距离测量）。与预期相反，更复杂的算法并不一定能够提高分类和变化检测的性能。建议对分类和变化检测应用使用简单的暗物体减法（带或不带瑞利大气校正或相对大气校正）。 (C) Elsevier Science Inc.，2001。保留所有权利。
The electromagnetic radiation (EMR) signals collected by statellites in the solar spectrum are modified by scattering and absorption by gases and aerosols while traveling through the atmosphere from the Earth's surface to the sensor. When and how to correct the atmospheric effects depend un the remote sensing and atmospheric data available, the information desired and the analytical methods used to extract the information In many applications involving classification and change detection, atmospheric correction is unnecessary as long as the training data and the data to be classified nl-e in the same relative scale. In other circumstances, corrections al-e mandatory to put multitemporal data on the same radio-metric scale in order to monitor terrestrial surfaces over time. A multitemporal dataset consisting of seven Land-sat 5 Thematic Mapper (TM) images from 1988 to 1996 of the Pearl River Delta, Guangdong Province, China was used to compare sewn absolute and one relative atmospheric correction algorithms with uncorrected raw data. Based on classification and change detection results all corrections improved the data analysis. The best overall results are achieved using a new method which adds the effect of Rayleigh scattering to conventional dark object subtraction. Though this method may not lead to accurate surface reflectance, it best minimizes the difference in reflectances within a land cover class through time as measured with the Jeffries-Matusita distance. Contrary to expectations, the more complicated algorithms do not necessarily lend to improved performance of classification and change detection. Simple dark object subtraction, with or without the Rayleigh atmosphere correction, or relative atmospheric correction are recommended for classification, and change detection applications. (C) Elsevier Science Inc., 2001. All Rights Reserved.