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ASTER polar cloud mask

机译:艾斯特北极云面膜

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An algorithm is currently under development that will provide a classification mask for ASTER imagery obtained poleward of 60 N and 60 S. The classification mask will be a product available through EOSDIS and is called the ASTER polar cloud mask. Ten classes are currently in the mask and include six clear classes (water, slush/wet ice, ice/snow, land, shadow on land, and shadow on ice/snow) and four cloud classes (thin cloud over ice/snow, water, or land, and thick cloud). The algorithms is designed as a four stage process. In the first stage the data are median filtered, sampled to 30 m spatial resolution, normalized, and navigated to coastlines and ancillary Earth surface databases. In the second stage, through adaptive thresholding, simple decision surfaces, and ancillary data, the class ambiguity of each pixel is reduced from ten to two to four classes. In the third stage, additional features are utilized in a paired- histogram classification methodology to make the final pixel classification. And finally, in the fourth stage, a simple spatial consistency check is performed over the entire classification mask to detect isolated pixel classifications. Over 3700 samples have been extracted and labeled to date representing over one million pixels from 82 Landsat TM circumpolar scenes. Tests of the algorithm on the labeled samples indicate that the clear/cloud classification accuracy is greater than 90 percent and subjective evaluation of the classification masks supports that result.
机译:目前正在开发算法,该算法将为ASTER图像提供一个分类掩模,用于获得60 n和60秒的杆子。分类掩模将是通过EOSDI可获得的产品,并且被称为烟雾偏振掩模。目前在面具中有十个课程,包括六个清晰的课程(水,泥,冰,冰/雪,陆,冰上的阴影,冰上/雪地上的阴影)和四个云课程(冰冷的云层(冰/雪)上,或土地和厚的云)。算法设计为四级过程。在第一阶段,数据是中值过滤,采样到30米的空间分辨率,标准化和导航到海岸线和辅助地面表面数据库。在第二阶段,通过自适应阈值,简单的判定表面和辅助数据,每个像素的类歧义从十到两到四个类减少。在第三阶段,在配对直方图分类方法中使用附加特征以进行最终的像素分类。最后,在第四阶段,在整个分类掩码上执行简单的空间一致性检查以检测隔离的像素分类。已经提取了超过3700个样本,并标记为从82个Landsat TM Circumpolar场景中表示超过一百万像素的迄今为止。标记样本上的算法的测试表明,清晰/云分类准确度大于90%,对分类掩码的主观评估支持该结果。

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