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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing >The ASTER polar cloud mask
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The ASTER polar cloud mask

机译：ASTER极地云面膜

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This research is concerned with the problem of producing polar cloud masks for satellite imagery. The results presented are for Thematic Mapper (TM) data from the northern and southern polar regions, however, the techniques discussed will be applied to ASTER data when it becomes available. A series of classification techniques have been implemented and tested, the most promising of which is a neural network classifier. To use a neural network classifier, the pixels in the data must be transformed into feature vectors, some of which are used for training the network and the remainder of which are reserved for testing the final system. The first challenge is the identification of pure pixel samples from the imagery. The Interactive Visual Image Classification System (IVICS) was developed specifically for this project to make this task simpler for the human expert. After labeling the pixels, the feature vectors are generated. One hundred and forty potential vector elements, consisting of linear and nonlinear combinations of the satellite channel data, have been identified. Because smaller input vectors reduce the difficulty of training and can improve classification accuracy, the set of potential vector elements must be reduced. Two techniques have been tested: a histogram-based selection method and a fuzzy logic method. Both have proven effective for this task. Although the polar region is the only area considered in this work, a system that can produce cloud masks for all areas of the globe will be required. Thus, speed, extensibility, and flexibility requirements must be added to the accuracy constraint. To achieve these goals, a two-stage classification approach is used. The first stage uses a series of static and adaptive thresholds derived from statistical analysis of the polar scenes to reduce the set of possible classes to which a pixel may be assigned, once a cluster of classes has been selected, a neural network trained to distinguish between the classes in the cluster is used to make the ultimate classification.
机译：这项研究涉及为卫星图像生产极光云掩模的问题。给出的结果适用于来自北极和南部极地地区的Thematic Mapper（TM）数据，但是，所讨论的技术将在ASTER数据可用时应用。已经实施和测试了一系列分类技术，其中最有前途的是神经网络分类器。要使用神经网络分类器，必须将数据中的像素转换为特征向量，其中一些特征向量用于训练网络，而其余部分则保留用于测试最终系统。第一个挑战是从图像中识别纯像素样本。交互式视觉图像分类系统（IVICS）是专门为该项目开发的，旨在使人类专家更轻松地完成此任务。在标记像素之后，生成特征向量。已经确定了一百四十个由卫星信道数据的线性和非线性组合组成的潜在矢量元素。因为较小的输入向量减少了训练的难度并可以提高分类精度，所以必须减少潜在向量元素的集合。已经测试了两种技术：基于直方图的选择方法和模糊逻辑方法。事实证明两者都有效。尽管极地地区是这项工作中考虑的唯一区域，但仍需要一个能够为全球所有区域生成云遮罩的系统。因此，速度，可扩展性和灵活性要求必须添加到精度约束中。为了实现这些目标，使用了两阶段分类方法。第一阶段使用从极地场景的统计分析得出的一系列静态和自适应阈值，以减少可能分配了像素的类的集合，一旦选择了类的集群，则训练一个神经网络以区分集群中的类用于进行最终分类。

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