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Onboard classification of remote sensing data can permit autonomous, intelligent scheduling decisions without ground interaction. In this study, we observe the sulfur-rich Borup-Fiord glacial springs in Canada with the Hyperion instrument aboard the EO-1 spacecraft. This system offers an analog to far more exotic locales such as Europa where remote sensing of biogenic indicators is of considerable...
Onboard classification of remote sensing data is of general interest given that it can be used as a trigger to initiate alarms, data download, additional higher-resolution scans, or more frequent scans of an area without ground interaction. In our case, we study the sulfur-rich Borup-Fiord glacial springs in Canada utilizing the Hyperion instrument aboard the EO-1 spacecraft. This system consists...
This paper presents PWEM, a technique for detecting class label noise in training data. PWEM detects mislabeled examples by assigning to each training example a probability that its label is correct. PWEM calculates this probability by clustering examples from pairs of classes together and analyzing the distribution of labels within each cluster to derive the probability of each label's correctness...
This paper presents two machine learning techniques that greatly reduce the number of person-hours required to generate high-quality training data for land cover classification. The first technique uses active learning to guide the generation of training data by selecting only the most informative examples for labeling. The second technique identifies and mitigates the impact of mislabeled instances...
The problem of finding unusual time series has recently attracted much attention, and several promising methods are now in the literature. However, virtually all proposed methods assume that the data reside in main memory. For many real-world problems this is not be the case. For example, in astronomy, multi-terabyte time series datasets are the norm. Most current algorithms faced with data which...
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