Cloud-free satellite image mosaics with regression trees and histogram matching
Cloud-free satellite image mosaics with regression trees and histogram matching
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DOI：
10.14358/pers.71.9.1079
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发表时间：
2005-09-01
影响因子：
1.3
通讯作者：
Ruefenacht, B
中科院分区：
文献类型：
--
作者：
Helmer, EH;Ruefenacht, B
无云光学卫星图像简化了遥感，但土地覆盖物候限制了现有的解决持续云层的方法，只能合成时间分辨率高、空间粗糙的图像。在这里，一种开发分辨率更高的无云图像的新战略允许简单的自动变化检测。该策略使用回归树来预测来自其他场景日期的参考场景中云和云阴影下的像素值。然后将改进的直方图匹配应用于相邻场景。在研究区域波多黎各、别克斯岛和库莱布拉岛，这一战略产生的陆地卫星图像马赛克仅通过光谱数据和最大似然分类就可以准确检测土地开发情况。在大约1991至2000年间，波多黎各的城市/建设用地增加了7.2%，别克斯和库莱布拉增加了49%。回归树建模和直方图匹配不需要人工解释。因此，它们可以支持大容量处理，以分发无云图像，用于使用常见分类器进行简单的变化检测。
Cloud-free optical satellite imagery simplifies remote sensing, but land-cover phenology limits existing solutions to persistent cloudiness to compositing temporally resolute, spatially coarser imagery. Here, a new strategy for developing cloud-free imagery at finer resolution permits simple automatic change detection. The strategy uses regression trees to predict pixel values underneath clouds and cloud shadows in reference scenes from other scene dates. It then applies improved histogram matching to adjacent scenes. In the study area, the islands of Puerto Rico, Vieques, and Culebra, Landsat image mosaics resulting from this strategy permit accurate detection of land development with only spectral data and maximum likelihood classification. Between about 1991 and 2000, urban/built-up lands increased by 7.2 percent in Puerto Rico and 49 percent in Vieques and Culebra. The regression tree modeling and histogram matching require no manual interpretation. Consequently, they can support large volume processing to distribute cloud free imagery for simple change detections with common classifiers.