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Classification of land based on analysis of remotely-sensed earth images

机译:基于遥感地球图像分析的土地分类

摘要

Land classification based on analysis of image data. Feature extraction techniques may be used to generate a feature stack corresponding to the image data to be classified. A user may identify training data from the image data from which a classification model may be generated using one or more machine learning techniques applied to one or more features of the image. In this regard, the classification module may in turn be used to classify pixels from the image data other than the training data. Additionally, quantifiable metrics regarding the accuracy and/or precision of the models may be provided for model evaluation and/or comparison. Additionally, the generation of models may be performed in a distributed system such that model creation and/or application may be distributed in a multi-user environment for collaborative and/or iterative approaches.
机译:基于图像数据分析的土地分类。特征提取技术可以用于生成与要分类的图像数据相对应的特征栈。用户可以使用应用于图像的一个或多个特征的一种或多种机器学习技术从图像数据中识别出训练数据,可以从该训练数据中生成分类模型。就这一点而言,分类模块可以继而用于从除训练数据之外的图像数据中对像素进行分类。另外,可以提供关于模型的准确性和/或精确度的可量化度量,以用于模型评估和/或比较。另外,可以在分布式系统中执行模型的生成,使得可以在多用户环境中为协作和/或迭代方法分布模型创建和/或应用。

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