Exploring uncertainty in remotely sensed data with parallel coordinate plots
Exploring uncertainty in remotely sensed data with parallel coordinate plots
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DOI：
10.1016/j.jag.2009.08.004
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发表时间：
2009-12
期刊：
影响因子：
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通讯作者：
Y. Ge;Sanping Li;V. Chris Lakhan;A. Lucieer
中科院分区：
文献类型：
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作者：
Y. Ge;Sanping Li;V. Chris Lakhan;A. Lucieer
由于分类遥感数据存在不确定性，因此有必要采用更好的技术来查明和显示各种程度的不确定性。因此，本文应用平行坐标图（PCP）的多维图形数据分析技术可视化的最大似然分类器（MLC）和模糊C均值（FCM）分类的陆地卫星专题制图（TM）数据的不确定性。Landsat TM数据来自中国山东省黄河三角洲。图像分类的MLC和FCM提供了每个像素的概率向量和模糊隶属度向量。基于这些向量，香农熵（S.E.）计算每个像素。然后为每一分类产出编制PCP。PCP轴表示后验概率向量和模糊隶属度向量，另外两个轴表示S. E。以及相关的不确定性程度。PCP突出了每个像元的不同土地覆盖类型的概率值的分布，也反映了具有不同程度的不确定性的像元的状态。然后将刷动功能添加到PCP可视化中，以突出显示所选的感兴趣像素。这不仅降低了可视化的不确定性，而且还提供了关于目标像素的位置和光谱特性的宝贵信息。
The existence of uncertainty in classified remotely sensed data necessitates the application of enhanced techniques for identifying and visualizing the various degrees of uncertainty. This paper, therefore, applies the multidimensional graphical data analysis technique of parallel coordinate plots (PCP) to visualize the uncertainty in Landsat Thematic Mapper (TM) data classified by the Maximum Likelihood Classifier (MLC) and Fuzzy C-Means (FCM). The Landsat TM data are from the Yellow River Delta, Shandong Province, China. Image classification with MLC and FCM provides the probability vector and fuzzy membership vector of each pixel. Based on these vectors, the Shannon's entropy (S.E.) of each pixel is calculated. PCPs are then produced for each classification output. The PCP axes denote the posterior probability vector and fuzzy membership vector and two additional axes represent S.E. and the associated degree of uncertainty. The PCPs highlight the distribution of probability values of different land cover types for each pixel, and also reflect the status of pixels with different degrees of uncertainty. Brushing functionality is then added to PCP visualization in order to highlight selected pixels of interest. This not only reduces the visualization uncertainty, but also provides invaluable information on the positional and spectral characteristics of targeted pixels.