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from Part II - Case Studies
Published online by Cambridge University Press: 29 May 2020
The last chapter using the dataset from chapter 6, this time the dataset is expanded using 80 thousand satellite images obtained from NASA for relative humidity density as it relates to vegetation. This image information is not visible information and thus pre-trained models are not useful for this problem. Instead, traditional computer vision techniques are showcased, involving histograms, local feature extraction, gradients and histograms of gradients. This domain exemplifies how to deal with high level of nuisance noise, how to normalize your features so that a small changes in the way the information was acquired does not completely throws off the underlining machine learning mechanism. These takeaways are valuable for anybody working with sensor data where the acquisition process has a high level of uncertainty. The domain also exemplifies working with large number of low-level sensor data.
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