...
首页> 外文期刊>Remote Sensing of Environment: An Interdisciplinary Journal >Improving Fmask cloud and cloud shadow detection in mountainous area for Landsats 4–8 images
【24h】

Improving Fmask cloud and cloud shadow detection in mountainous area for Landsats 4–8 images

机译:在山区的山区山区改善Fmask云和云阴影检测4-8图像

获取原文
获取原文并翻译 | 示例
   

获取外文期刊封面封底 >>

       

摘要

Abstract We developed a new algorithm called MFmask (Mountainous Fmask) for automated cloud and cloud shadow detection for Landsats 4–8 images acquired in mountainous areas. The MFmask algorithm, built upon the success of the Fmask algorithm (Zhu and Woodcock, 2012; Zhu et al., 2015), is designed for cloud and cloud shadow detection in mountainous areas, where the Fmask algorithm is not performing well. The inputs of the MFmask algorithm include Landsat Top of Atmosphere (TOA) reflectance, Brightness Temperature (BT), and Digital Elevation Models (DEMs). Compared to Fmask, MFmask can separate water and land pixels better in mountainous areas with the aid of DEMs. Moreover, MFmask produces better cloud detection results than Fmask in mountainous areas after BT is linearly normalized by DEMs. To provide more accurate cloud shadow detection in mountainous areas, MFmask uses a double-projection approach to better predict cloud shadow shape on slope side. Additionally, MFmask applies a topographic correction to remove terrain shadows and estimates cloud base height with neighboring clouds. Both will reduce the possibility of cloud and cloud shadow mismatch and increase cloud shadow detection accuracy for places with large topographic gradient. To test the performance of the proposed MFmask algorithm, a total of 67 Landsat images acquired in mountainous areas from different parts of the world were selected for assessing the accuracy of cloud detection, in which 15 of them were used for assessing the accuracy of cloud shadow detection. Compared with Fmask, MFmask can provide substantial improvements in cloud and cloud shadow detection accuracies for places with large topographic gradient and also work well for relatively flat terrain. Highlights ? Cloud and cloud shadow detection for Landsats 4–8 images in mountainous areas. ? It is developed by incorporating DEM data into the Fmask algorithm. ? Better cloud and cloud shadow detection results are observed for mountainous areas. ? It is also applicative for Landsat images acquired in non-mountainous areas. ]]>
机译:<![cdata [ 抽象 我们开发了一种新的算法,称为MFMask(山区FMASK),用于自动云和云阴影检测Landsats 4-8在山区获得的图像。 MFMask算法基于FMASK算法的成功(朱和伍德克,2012年; Zhu等,2015),专为云和云阴影检测而设计,在山区,Fmask算法不顺利。 MFMask算法的输入包括大气层(TOA)反射率,亮度温度(BT)和数字高度模型(DEM)的旧山顶顶部。与FMASK相比,MFMASK可以借助DEM在山区分离水和土地像素。此外,在BT线性标准化之后,MFMask在山区被DEM线性标准化之后产生比山区的Fmask更好的云检测结果。为了在山区提供更准确的云阴影检测,MFMask使用双重投影方法来更好地预测斜面侧的云阴影形状。此外,MFMask应用地形校正以删除地形阴影并估计云基高度与邻近的云。两者都将减少云和云阴影不匹配的可能性,并增加具有大地形梯度的地方的云阴影检测精度。为了测试所提出的MFMask算法的性能,选择了从世界各地的山区获得的67个Landsat图像,以评估云检测的准确性,其中15个用于评估云阴影的准确性检测。与Fmask相比,MFMask可以为具有大地形梯度的地方提供大量的云和云阴影检测精度,并且对于相对平坦的地形也适用。 亮点 < CE:标签>? 山区Landsats 4-8图像的云和云阴影检测。 它是通过结合dem开发的数据进入FMask算法。 山区观察到更好的云和云阴影检测结果。 它也是在非山区中获取的Landsat图像的应用。 ]]>

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号