Predicting Classification Performance for Benchmark Hyperspectral Datasets

• Published in: IEEE journal of selected topics in applied earth observations and remote sensing Vol. 15; pp. 4180 - 4193
• Main Authors: Zhao, Bin, Ragnarsson, Haukur Isfeld, Ulfarsson, Magnus O., Cavallaro, Gabriele, Benediktsson, Jon Atli
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Benchmarks; Classification; Classification algorithms; Classifiers; Datasets; Feature extraction; Hyperspectral image (HSI) classification; Hyperspectral imaging; Image classification; Metadata; Methods; Performance prediction; prediction; Prediction algorithms; Predictive models; Remote sensing; Scientific papers; Training
• ISSN: 1939-1404; 2151-1535
• DOI: 10.1109/JSTARS.2022.3173893

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.