TY  - JOUR
AU  - Zhao, Bin
AU  - Ragnarsson, Haukur Isfeld
AU  - Ulfarsson, Magnus O.
AU  - Cavallaro, Gabriele
AU  - Benediktsson, Jon Atli
TI  - Predicting Classification Performance for Benchmark Hyperspectral Datasets
JO  - IEEE journal of selected topics in applied earth observations and remote sensing
VL  - 15
SN  - 1939-1404
CY  - New York, NY
PB  - IEEE
M1  - FZJ-2022-02983
SP  - 4180 - 4193
PY  - 2022
AB  - The 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.
LB  - PUB:(DE-HGF)16
UR  - <Go to ISI:>//WOS:000805801800006
DO  - DOI:10.1109/JSTARS.2022.3173893
UR  - https://juser.fz-juelich.de/record/909065
ER  -