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AN UNSUPERVISED LABELING APPROACH FOR HYPERSPECTRAL IMAGE CLASSIFICATION
2020
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Abstract. The application of hyperspectral image analysis for land cover classification is mainly executed in presence of manually labeled data. The ground truth represents the distribution of the actual classes and it is mostly derived from field recorded information. Its manual generation is ineffective, tedious and very time-consuming. The continuously increasing amount of proprietary and publicly available datasets makes it imperative to reduce these related costs. In addition, adequately
doi:10.5194/isprs-archives-xliii-b3-2020-407-2020
fatcat:42he4tnuwreybnpu23ru7ko6li