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Mining spectral libraries to study sensors' discrimination ability

机译：挖掘光谱库以研究传感器的识别能力

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In remote sensing data classification, the ability to discriminate different land cover or material types is directly linked with the spectral resolution and sampling provided by the optical sensor.1 Several previous studies~(2-4) showed that the spectral resolution is a critical issue, especially to discriminate different land covers in urban areas. In spite of the increasing availability of hyperspectral data, multispectral optical sensors on board of several satellites are still acquiring everyday a massive amount of data with a relatively poor spectral resolution (i.e. usually about 4 to 7 spectral bands). These remotely sensed data are intensively used for Earth observation regardless of their limited spectral resolution.rnIn this paper, we propose to study the capacity of discrimination of several of these optical sensors : Pleiades, QuickBird, SPOT5, Ikonos, Landsat TM, Formosat and Meris. To achieve this goal, we used different spectral libraries which provide spectra of materials and land covers generally with a fine spectral resolution. These spectra were extracted from these libraries and convolved with the Relative Spectral Responses (RSR) of each sensor to create spectra at the sensors' resolutions. Then, these reduced spectra were evaluated thanks to a classical separability index and machine learning tools. This study focuses on the capacity of each sensor to discriminate different materials according to its spectral resolution. As the spectra for each sensor are created from the exact same spectra extracted from the libraries, the only variation is the RSR of the sensors. This approach allows us to fairly compare the different ability of the sensors to discriminate materials.
机译：在遥感数据分类中，区分不同土地覆被或材料类型的能力与光学传感器提供的光谱分辨率和采样直接相关。1先前的一些研究〜（2-4）表明光谱分辨率是一个关键问题，尤其是区分城市地区的不同土地覆盖。尽管高光谱数据的可用性不断增加，但几颗卫星上的多光谱光学传感器仍每天仍在以相对较差的光谱分辨率（即通常约4至7个光谱带）采集大量数据。这些遥感数据无论其光谱分辨率如何，都被广泛用于地球观测。rn本文建议研究以下几种光学传感器的辨别能力：P宿星，QuickBird，SPOT5，Ikonos，Landsat TM，Formosat和Meris 。为了实现此目标，我们使用了不同的光谱库，这些光谱库通常以良好的光谱分辨率提供材料和土地覆盖物的光谱。这些光谱是从这些库中提取的，并与每个传感器的相对光谱响应（RSR）卷积在一起，以传感器的分辨率创建光谱。然后，借助经典的可分离性指数和机器学习工具对这些减少的光谱进行了评估。这项研究着重于每个传感器根据其光谱分辨率区分不同材料的能力。由于每个传感器的光谱都是从库中提取的完全相同的光谱创建的，因此唯一的变化就是传感器的RSR。这种方法使我们能够公平地比较传感器区分材料的不同能力。

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