Normalization method for multi-sensor high spatial and temporal resolution satellite imagery with radiometric inconsistencies
Normalization method for multi-sensor high spatial and temporal resolution satellite imagery with radiometric inconsistencies
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DOI：
10.1016/j.compag.2019.104893
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发表时间：
2019-09-01
影响因子：
8.3
通讯作者：
Obrknezev, Nikola
中科院分区：
文献类型：
--
作者：
Leach, Nicholas;Coops, Nicholas C.;Obrknezev, Nikola
小型卫星分布式系统正在产生一种新型的遥感数据：具有高空间和时间分辨率和几乎每天覆盖全球的多光谱数据。然而，为了实现这一目的的广泛应用，分布式系统或星座中的所有卫星都必须获得具有精确地理参考和一致辐射特性的图像。在这项研究中，我们开发了一种方法来自动配准和辐射归一化的时间密集的一系列小卫星图像使用地理校正的参考图像。为了证明这种方法，我们规范化的smallsat图像的时间序列在像素和多边形的水平，平滑的光谱指数通过时间来检测突变和渐变。使用PlanetScope图像，我们在加拿大不列颠哥伦比亚省的森林地区测试了这些方法，该地区在2017年受到森林火灾的严重影响。通过研究火灾前后所获得的图像中的归一化植被指数（NDVI），我们发现，这种方法可以简单地识别燃烧和未燃烧的地区，这是不容易不应用归一化方法。我们的研究结果表明，所开发的方法可以帮助充分利用遥感数据集，具有高空间分辨率和高频率，但可能包含辐射不一致，以快速识别土地覆盖变化。
Distributed systems of small satellites are generating a new type of remote sensing data: multi-spectral data with high spatial and temporal resolution and near-daily global coverage, This data is proving to be valuable for monitoring land cover change. However, in order to achieve widespread application for this purpose, all satellites in the distributed system, or constellation, must acquire imagery with accurate georeferencing and consistent radiometric properties. In this research, we developed a method to automatically co-register and radiometrically normalize a temporally dense series of smallsat images using geocorrected reference imagery. To demonstrate the approach, we normalized a smallsat image time series at both the pixel and the polygon level, smoothing spectral indices through time to detect both abrupt and gradual changes. Using PlanetScope imagery, we tested these methods in a forested region of British Columbia, Canada heavily impacted by forest fires in 2017. By examining the normalized difference vegetation index (NDVI) in the acquired imagery before and after the fires, we found that this method allowed simple identification of burned and unburned areas, which was not readily possible without applying the normalization method. Our result suggests that the developed approach can help fully exploit remote sensing datasets that have high spatial resolution and are acquired with high frequency but potentially contain radiometric inconsistencies in order to quickly identify land cover changes.