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Optimal use of polarimetric signature on PALSAR-2 data for land cover classification

机译：PALSAR-2数据上极化特征的最佳利用以进行土地覆盖分类

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SAR data is playing key role in monitoring, the current status or change in, the land cover. For unsupervised SAR image classification, polarization signatures can play a significant role. Since it is difficult to obtain specific polarization signature of real land cover, it is customary to represent them with standard canonical structures polarization signatures. A critical analysis of the complex signatures of real targets is essential thereafter it is also a challenge to decide the thresholds or class boundary value on the correlation images. Therefore, in this paper an attempt has been made to critically analyze the polarimetric signature of complex targets and based on the correlation image analysis an OTSU multi-thresholding based approach is proposed to decide the individual class boundary values which will finally help in building a decision tree (DT) based classification technique. For this purpose L band fully polarimetric SAR data (PALSAR-2) has been used. DT class thresholds are computed using OTSU multi-thresholding method, scatter plot method, and a priori information. Obtained results reveal that complementary features like polarization signatures can help in identification as well as classification of land surface objects significantly by the proposed method.
机译：SAR数据在监测土地覆被的当前状态或变化中起着关键作用。对于无监督的SAR图像分类，极化特征可以发挥重要作用。由于很难获得真实土地覆被的特定极化特征，因此习惯用标准规范结构的极化特征来表示它们。对真实目标的复杂特征进行严格分析至关重要，此后，确定相关图像上的阈值或类别边界值也是一项挑战。因此，本文尝试对复杂目标的极化特征进行严格分析，并基于相关图像分析，提出了基于OTSU多阈值的方法来确定各个类的边界值，这最终将有助于建立决策。基于树（DT）的分类技术。为此，使用了L波段全极化SAR数据（PALSAR-2）。 DT类阈值是使用OTSU多阈值方法，散点图方法和先验信息来计算的。所得结果表明，通过极化方法，互补特征（如极化特征）可以显着帮助识别和分类地表物体。

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