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首页> 外文期刊>Remote Sensing of Environment: An Interdisciplinary Journal >An image transform to characterize and compensate for spatial variations in thin cloud contamination of Landsat images
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An image transform to characterize and compensate for spatial variations in thin cloud contamination of Landsat images

机译：图像变换可表征和补偿Landsat图像的薄云污染中的空间变化

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摘要

A haze optimized transformation (HOT) is developed assessed for the detection and characterization of haze/cloud spatial distributions in Landsat scenes. The transformation is derived from an analysis of a visible-band space where spectral response to diverse surface cover classes under clear-sky conditions is highly correlated, but spectral response to haze is highly sensitive to both optical wavelength and haze optical depth. The robustness of the detection algorithm is demonstrated through its application to visible band imagery of seven Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) scenes that encompass diverse surface cover and atmospheric characteristics. A methodology for utilizing the transformed image to radiometrically compensate visible band imagery is presented and quantitatively tested in the correction of an example ETM+ scene.
机译：开发了雾度优化转换（HOT），用于检测和表征Landsat场景中的雾度/云空间分布。该变换来自对可见带空间的分析，其中在晴朗的天空条件下，对不同表面覆盖类别的光谱响应高度相关，但是对雾度的光谱响应对光波长和雾度光学高度高度敏感。通过将其应用于包含不同表面覆盖和大气特征的七个Landsat 5 Thematic Mapper（TM）和Landsat 7 Enhanced Thematic Mapper Plus（ETM +）场景的可见波段图像，证明了该检测算法的鲁棒性。在示例ETM +场景的校正中，提出并定量测试了利用变换后的图像进行辐射测量补偿可见带图像的方法。

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