基于NDFI和改进泡利分解技术的多传感器棕榈油种植园与森林覆盖分离方法

E. Muñoz, A. Zozaya, E. Lindquist
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引用次数: 4

摘要

在这项工作中,提出了一种使用NDFI和改进的泡利分解技术的多传感器方法来分离油棕种植园和森林覆盖。本研究的主要贡献在于,在自动化监督分类算法的背景下,有可能减少这两类的误分类,减少森林覆盖检测和制图过程中产生的不确定性。本文提出的方法包括从高分辨率多光谱卫星图像中生成定义阈值的原始森林地图覆盖,然后通过泡利分解方法利用散射机制从该分类中过滤出棕榈油种植园。初步结果表明,该方法能够生成补充信息,将油棕种植园与森林覆盖分类分开。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Multi-Sensor Approach to Separate Palm Oil Plantations from Forest Cover Using NDFI and a Modified Pauli Decomposition Technique
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.
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