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A Multi-Sensor Approach to Separate Palm Oil Plantations from Forest Cover Using NDFI and a Modified Pauli Decomposition Technique

机译:使用NDFI和改进的Pauli分解技术将棕榈油种植园分离棕榈油种植园的多传感器方法

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In this work, a multi-sensor approach to separate oil palm plantations from forest cover using NDFI and a modified Pauli Decomposition technique is presented. The main contribution of this research is the potential to reduce misclassification of both classes, in the context of automated-base supervised classification algorithms, to decrease uncertainties derived through the detection and mapping process of forest cover. The hereby proposed method includes the generation of a primary forest map cover defining thresholds from a high resolution multi -spectral satellite image, and then the palm oil plantation will be filtered out from this classification using scattering mechanisms by a Pauli Decomposition approach. Preliminary results shown the capabilities of this approach in order to generate complementary information to separate the oil palm plantations from the forest cover classification.
机译:在这项工作中,提出了一种使用NDFI和改进的Pauli分解技术将来自森林覆盖物和改进的Pauli分解技术分离的多传感器方法。本研究的主要贡献是在自动基础监督分类算法的背景下减少两类错误分类的可能性,以减少通过森林覆盖的检测和绘制过程来实现的不确定性。特此提出的方法包括产生从高分辨率多光谱卫星图像定义阈值的主要林地图覆盖,然后使用Pauli分解方法使用散射机制从该分类中过滤出棕榈油种植园。初步结果显示了这种方法的能力,以便从森林覆盖分类中分离互补信息以将油棕种植园分离。

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