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Distributed Computing Approach for Remote Sensing Data

机译：用于遥感数据的分布式计算方法

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Processing image data generated by new remote sensing systems can severely tax the computational limits of the classic single processor systems that are normally available to the remote sensing practitioner. Operating on these large data sets with a single computer system sometimes means that simplifying approximations are used that can limit the precision of the final results. For instance, in supervised classification it is often necessary to assume a Gaussian structure for the data. While this assumption has the advantage of greatly reducing the amount of pixels that must be processed this abstraction can also mask important structures in the raw data. Recent work at Pacific Northwest National Laboratory strongly suggests that a distributed network of inexpensive PCs can be designed that is optimal to deal with the type of computationally intensive problems encountered in processing remotely sensed images. Under the assumption that this new type of distributed computing will remove computational constraints, new image processing algorithms for remote sensed images are now being considered. A specific example will be presented, where consideration of the entire training set, instead of abstracting of the training set to a few representative parameters, can significantly improve classification algorithms.
机译：由新的遥感系统生成的处理图像数据可以严重征税，该系统通常可用于遥感从业者的经典单处理器系统。使用单个计算机系统上的这些大数据集操作有时意味着使用可以限制最终结果的精度的简化近似值。例如，在监督分类中，通常需要假设用于数据的高斯结构。虽然此假设具有大大减少必须处理的像素量的优点，但此抽象也可以掩盖原始数据中的重要结构。最近在太平洋西北国家实验室的工作强烈建议，可以设计一个廉价的PC的分布式网络，这是最佳的，以处理远程感测图像中遇到的计算密集型问题的类型。在该新类型的分布式计算将去除计算约束的假设，现在正在考虑用于远程感测图像的新图像处理算法。将提出一个具体的示例，其中考虑整个训练集，而不是将训练设置为少数代表参数的培训，可以显着改善分类算法。

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