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PEATLAND DELINEATION UNDER FOREST CANOPY WITH POLSAR DATA USING MODEL BASED DECOMPOSITION TECHNIQUE

机译:泥炭地描绘在森林冠层下,使用基于模型的分解技术的Polsar数据

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The paper describes evaluating the potential of peatland detection under forest canopy with L-band space borne quad-polarization data in the boreal forest zone. Particular emphasis was made on under what seasonal conditions this detection was possible using single SAR data-take. For this purpose multitemporal ALOS PALSAR imagery acquired over Kuortane test site in central Finland during 2007-2008 was used. Supervised classification experiments employing selected polarimetric features were performed using standard maximum likelihood approach and probabilistic neural network (PNN). Strong non-gaussianity effects were noted, with better performance demonstrated by PNN, utilizing non-parametric estimation of probability distributions of the respective polarimetric features. Suitability of several techniques aimed at compensating the presence of forest canopy was studied as well.
机译:本文介绍了利用北方林区L波段空间在森林冠层下泥土检测的潜力。在使用单个SAR数据采取的季节性条件下,在季节性条件下进行特别强调。为此目的,使用了在2007 - 2008年期间在芬兰中央芬兰的Kuortane测试场所获得的Multimporal Alos Palsar图像。使用标准的最大似然方法和概率神经网络(PNN)进行采用所选偏振特征的监督分类实验。注意到强大的非高斯度效应,PNN展示了更好的性能,利用各个偏振特征的概率分布的非参数估计。还研究了旨在补偿森林冠层存在的几种技术的适用性。

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