A multi-temporal method for cloud detection, applied to FORMOSAT-2, VENμS, LANDSAT and SENTINEL-2 images
A multi-temporal method for cloud detection, applied to FORMOSAT-2, VENμS, LANDSAT and SENTINEL-2 images
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DOI：
10.1016/j.rse.2010.03.002
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发表时间：
2010-08-16
影响因子：
13.5
通讯作者：
Dedieu, G.
中科院分区：
文献类型：
--
作者：
Hagolle, O.;Huc, M.;Dedieu, G.
在陆地上，遥感图像上的云检测并不是一件容易的事情，因为即使在高分辨率下，也很难将云与底层景观区分开来。到目前为止，大多数高分辨率图像都是在没有相关云掩模的情况下发布的。由于两个新的卫星飞行任务将提供结合三个特点的光学图像：高空间分辨率、高重访频率和恒定视角，这种情况在不久的将来应该会改变。VEN mu S（法国和以色列合作）使命将于2012年发射，欧洲SENTINEL-2使命将于2013年发射。幸运的是，两个现有的卫星任务，FORMOSAT-2和LANDSAT，能够模拟这些传感器的未来数据。在恒定视角的多时相图像提供了一种新的方法来区分有云和无云的像素，使用相对稳定的地球表面反射率相比，受云影响的像素的反射率的快速变化。在这项研究中，我们使用时间序列的图像FORMOSAT-2和LANDSAT开发和测试的多时相云检测（MTCD）方法。该算法结合了一个突然增加的反射率在蓝色波长上的一个像素的基础上的检测，和一个测试的线性相关性的像素邻域采取从夫妇的连续获得的images.MTCD云掩模进行了比较，从FORMOSAT-2和LANDSAT数据目录获得的云量评估。结果表明，MTCD方法提供了一个更好的区分有云和无云像素比通常的方法的基础上施加到反射率或反射率比阈值。该方法将在VEN mu S 2级处理中使用，并将建议用于SENTINEL-2 2级处理。(C)2010年爱思唯尔公司All rights reserved.
Over lands, the cloud detection on remote sensing images is not an easy task, because of the frequent difficulty to distinguish clouds from the underlying landscape, even at a high resolution. Up to now, most high resolution images have been distributed without an associated cloud mask. This situation should change in the near future, thanks to two new satellite missions that will provide optical images combining 3 features: high spatial resolution, high revisit frequency and constant viewing angles. The VEN mu S (French and Israeli cooperation) mission should be launched in 2012 and the European SENTINEL-2 mission in 2013. Fortunately, two existing satellite missions, FORMOSAT-2 and LANDSAT, enable to simulate the future data of these sensors.Multi-temporal imagery at constant viewing angles provides a new way to discriminate clouded and unclouded pixels, using the relative stability of the earth surface reflectances compared to the quick variations of the reflectance of pixels affected by clouds. In this study, we have used time series of images from FORMOSAT-2 and LANDSAT to develop and test a Multi-Temporal Cloud Detection (MTCD) method. This algorithm combines a detection of a sudden increase of reflectance in the blue wavelength on a pixel by pixel basis, and a test of the linear correlation of pixel neighborhoods taken from couples of images acquired successively.MTCD cloud masks are compared with cloud cover assessments obtained from FORMOSAT-2 and LANDSAT data catalogs. The results show that the MTCD method provides a better discrimination of clouded and unclouded pixels than the usual methods based on thresholds applied to reflectances or reflectance ratios. This method will be used within VEN mu S level 2 processing and will be proposed for SENTINEL-2 level 2 processing. (C) 2010 Elsevier Inc. All rights reserved.