Building An Operationally Relevant Dataset From Satellite Imagery
2019 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2019)(2019)
摘要
A set of labeled training and testing data is required for most algorithmic approaches to image classification, and it is well known that data collection and curation is one of the biggest roadblocks to developing classification systems for real world applications. The quality of the dataset has a direct impact on the effectiveness of a trained algorithm for a given application. In this work we describe a dataset that we created to train algorithms to classify ships in overhead satellite imagery. Our experience revealed to us many challenges associated with datasets and with classification systems in general, which we outline as a cautionary tale to others embarking on a similar task.
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关键词
operationally relevant dataset,image classification,data collection,trained algorithm,overhead satellite imagery,ship classification systems,computer vision,Naval Information Warfare Center Pacific,NIWC Pacific,United States Navy research laboratory,BCCT dataset,barge,cargo ship,container ship,tanker
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