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000909065 1001_ $$0P:(DE-HGF)0$$aZhao, Bin$$b0
000909065 245__ $$aPredicting Classification Performance for Benchmark Hyperspectral Datasets
000909065 260__ $$aNew York, NY$$bIEEE$$c2022
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000909065 520__ $$aThe classification of hyperspectral images (HSIs) is an essential application of remote sensing and it is addressed by numerous publications every year. A large body of these papers present new classification algorithms and benchmark them against established methods on public hyperspectral datasets. The metadata contained in these research papers (i.e., the size of the image, the number of classes, the type of classifier, etc.) present an unexploited source of information that can be used to estimate the performance of classifiers before doing the actual experiments. In this article, we propose a novel approach to investigate to what degree HSIs can be classified by using only metadata. This can guide remote sensing researchers to identify optimal classifiers and develop new algorithms. In the experiments, different linear and nonlinear prediction methods are trained and tested by using data on classification accuracy and metadata from 100 HSIs classification papers. The experimental results demonstrate that the proposed ensemble learning voting method outperforms other comparative methods in quantitative assessments.
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000909065 7001_ $$0P:(DE-HGF)0$$aRagnarsson, Haukur Isfeld$$b1
000909065 7001_ $$00000-0002-0461-040X$$aUlfarsson, Magnus O.$$b2
000909065 7001_ $$0P:(DE-Juel1)171343$$aCavallaro, Gabriele$$b3
000909065 7001_ $$0P:(DE-HGF)0$$aBenediktsson, Jon Atli$$b4$$eCorresponding author
000909065 773__ $$0PERI:(DE-600)2457423-5$$a10.1109/JSTARS.2022.3173893$$gVol. 15, p. 4180 - 4193$$p4180 - 4193$$tIEEE journal of selected topics in applied earth observations and remote sensing$$v15$$x1939-1404$$y2022
000909065 8564_ $$uhttps://juser.fz-juelich.de/record/909065/files/Predicting_Classification_Performance_for_Benchmark_Hyperspectral_Datasets.pdf$$yOpenAccess
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000909065 9141_ $$y2022
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