Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/JSTARS.2022.3173893 | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING |
Keywords | DocType | Volume |
Training, Predictive models, Metadata, Classification algorithms, Hyperspectral imaging, Feature extraction, Prediction algorithms, Hyperspectral image (HSI) classification, pre- diction, remote sensing | Journal | 15 |
ISSN | Citations | PageRank |
1939-1404 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bin Zhao | 1 | 0 | 1.01 |
| Haukur Isfeld Ragnarsson | 2 | 0 | 0.34 |
| Magnus O. Ulfarsson | 3 | 0 | 0.34 |
| Gabriele Cavallaro | 4 | 0 | 0.68 |
| Jon Atli Benediktsson | 5 | 4064 | 251.17 |