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首页> 外文期刊>International journal of remote sensing >Radiometric correction effects in Landsat multi-date/multi-sensor change detection studies
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Radiometric correction effects in Landsat multi-date/multi-sensor change detection studies

机译:Landsat多日期/多传感器变化检测研究中的辐射校正效果

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Radiometric corrections serve to remove the effects that alter the spectral characteristics of land features, except for actual changes in ground target, becoming mandatory in multi-sensor, multi-date studies. In this paper, we evaluate the effects of two types of radiometric correction methods (absolute and relative) for the determination of land cover changes, using Landsat TM and Landsat ETM + images. In addition, we present an improvement made to the relative correction method addressed. Absolute correction includes a cross-calibration between TM and ETM+ images, and the application of an atmospheric correction protocol. Relative correction normalizes the images using pseudo-invariant features (PIFs) selected through band-to-band PCA analysis. We present a new algorithm for PIFs selection in order to improve normalization results. A post-correction evaluation index (Quadratic Difference Index (QD)), and post-classification and change detection results were used to evaluate the performance of the methods. Only the absolute correction method and the new relative correction method presented in this paper show good post-correction and post-classification results (QD index ≈ 0; overall accuracy > 80%; kappa > 0.65) for all the images used. Land cover change estimations based on uncorrected images present unrealistic change rates (two to three times those obtained with corrected images), which highlights the fact that radiometric corrections are necessary in multi-date multi-sensor land cover change analysis.
机译:辐射校正可以消除改变土地特征光谱特征的影响,但地面目标的实际变化除外,这在多传感器,多日期研究中成为强制性要求。在本文中,我们使用Landsat TM和Landsat ETM +图像评估了两种辐射测定校正方法(绝对方法和相对方法)对确定土地覆盖变化的影响。此外,我们提出了一种改进的相对校正方法。绝对校正包括TM和ETM +图像之间的交叉校正以及大气校正协议的应用。相对校正使用通过频带间PCA分析选择的伪不变特征(PIF)对图像进行归一化。我们提出了一种用于PIF选择的新算法,以提高归一化结果。校正后评估指数(二次方差指数(QD))以及分类后和变更检测结果用于评估方法的性能。对于本文中使用的所有图像,只有本文提出的绝对校正方法和新的相对校正方法才能显示出良好的校正后和分类后的结果(QD指数≈0;总体精度> 80%; kappa> 0.65)。基于未校正图像的土地覆盖变化估算显示出不切实际的变化率(是使用校正后的图像获得的变化率的2至3倍),这凸显了在多日期多传感器土地覆盖变化分析中必须进行辐射校正的事实。

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