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Autonomous selective downlink algorithms can choose informative subsets of data to improve the science value of these bandwidth-limited transmissions. This requires statistical descriptors of the data that reflect very abstract and subtle distinctions in science content. We propose a metric learning strategy that teaches algorithms how best to cluster new data based on training examples supplied by domain scientists. We demonstrate that clustering informed by metric learning produces results that more closely match multiple scientists\u2019 labelings of aerial data than do clusterings based on random or periodic sampling. A new metric-learning strategy accommodates training sets produced by multiple scientists with different and potentially inconsistent mission objectives. Our methods are fit for current spacecraft processors (e.g., RAD750) and would further benefit from more advanced spacecraft processor architectures, such as OPERA.<\/jats:p>","DOI":"10.1145\/2168752.2168765","type":"journal-article","created":{"date-parts":[[2012,10,12]],"date-time":"2012-10-12T20:56:02Z","timestamp":1350075362000},"page":"1-19","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Using Clustering and Metric Learning to Improve Science Return of Remote Sensed Imagery"],"prefix":"10.1145","volume":"3","author":[{"given":"David S.","family":"Hayden","sequence":"first","affiliation":[{"name":"Jet Propulsion Laboratory, California Institute of Technology"}]},{"given":"Steve","family":"Chien","sequence":"additional","affiliation":[{"name":"Jet Propulsion Laboratory, California Institute of Technology"}]},{"given":"David R.","family":"Thompson","sequence":"additional","affiliation":[{"name":"Jet Propulsion Laboratory, California Institute of Technology"}]},{"given":"Rebecca","family":"Casta\u00f1o","sequence":"additional","affiliation":[{"name":"Jet Propulsion Laboratory, California Institute of Technology"}]}],"member":"320","published-online":{"date-parts":[[2012,5]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/FOCS.2009.14"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/0167-8191(90)90031-4"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2008.07.003"},{"volume-title":"Proceedings of the International Symposium on Artificial Intelligence, Robotics and Automation in Space (iSAIRAS).","author":"Calderon F.","key":"e_1_2_1_4_1"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00138-007-0081-3"},{"volume-title":"Proceedings of the ICML Workshop on Machine Learning Technologies for Autonomous Space.","author":"Casta\u00f1o R.","key":"e_1_2_1_6_1"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1002\/rob.v24:5"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/1281192.1281209"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0031-3203(99)00139-9"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.2514\/1.12923"},{"volume-title":"Proceedings of the Performance Metrics for Intelligent Systems Workshop.","year":"2002","author":"Clough B.","key":"e_1_2_1_11_1"},{"key":"e_1_2_1_12_1","unstructured":"Duda R. 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