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Remote characterization of fuel types using multi and hyper-spectral data

机译:使用多光谱和高光谱数据远程表征燃料类型

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This study aims to ascertain how well remote sensing data can characterize fuel type at different spatial scales in fragmented ecosystems. For this purpose, multisensor and multiscale remote sensing data such as, hyperspectral (Multispectral Infrared and Visible Imaging Spectrometer) MIVIS and Landsat- Temathic Mapper (TM) acquired in 1998 were analysed for a test area of Southern Italy characterized by mixed vegetation covers and complex topography. Fieldwork fuel type recognition, performed at the same time as remote sensing data acquisitions, were used to assess the results obtained for the considered test areas..rnThe method comprised the following three steps: (Ⅰ) adaptation of Prometheus fuel types for obtaining a standardization system useful for remotely sensed classification of fuel types and properties in the considered Mediterranean ecosystems; (Ⅱ) model construction for the spectral characterization and mapping of fuel types; (Ⅲ) accuracy assessment for the performance evaluation based on the comparison of satellite-based results with ground-truth.rnTwo different approaches have been adopted for fuel type mapping: the well-established classification techniques and spectral mixture analysis. Results from preliminary analysis have showed that the use of unmixing techniques allows an increase in accuracy at around 7% compared to the accuracy level obtained by applying a widely used classification algorithm.
机译:这项研究的目的是确定在零散的生态系统中,遥感数据在不同空间尺度上能较好地描述燃料类型的特征。为此,分析了1998年获得的高光谱(多光谱红外和可见光谱仪)MIVIS和Landsat-Temathic Mapper(TM)等多传感器和多尺度遥感数据,分析了意大利南部测试区域的植被覆盖和复杂情况。地形。与遥感数据采集同时进行的野外工作燃料类型识别被用于评估在所考虑的测试区域获得的结果。方法包括以下三个步骤:(I)适应普罗米修斯燃料类型以获得标准化该系统可用于对所考虑的地中海生态系统中的燃料类型和特性进行遥感分类; (二)燃料种类光谱表征和制图的模型构建; (Ⅲ)基于卫星结果与地面真实性的比较,用于性能评估的准确性评估。rn燃料类型映射采用了两种不同的方法:完善的分类技术和光谱混合分析。初步分析的结果表明,与应用广泛使用的分类算法所获得的准确度水平相比,使用混合技术可以使准确度提高约7%。

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