Iterative Training Sample Expansion to Increase and Balance the Accuracy of Land Classification from VHR Imagery

Lv, Zhiyong; Li, Guangfei; Jin, Zhenong; Benediktsson, Jon Atli; Foody, Giles M.

Abstract

© 1980-2012 IEEE. Imbalanced training sets are known to produce suboptimal maps for supervised classification. Therefore, one challenge in mapping land cover is acquiring training data that will allow classification with high overall accuracy (OA) in which each class is also mapped onto similar user's accuracy. To solve this problem, we integrated local adaptive region and box-and-whisker plot (BP) techniques into an iterative algorithm to expand the size of the training sample for selected classes in this article. The major steps of the proposed algorithm are as follows. First, a very small initial training sample (ITS) for each class set is labeled manually. Second, potential new training samples are found within an adaptive region by conducting local spectral variation analysis. Lastly, three new training samples are acquired to capture information regarding intraclass variation; these samples lie in the lower, median, and upper quartiles of BP. After adding these new training samples to the ITS, classification is retrained and the process is continued iteratively until termination. The proposed approach was applied to three very high-resolution (VHR) remote-sensing images and compared with a set of cognate methods. The comparison demonstrated that the proposed approach produced the best result in terms of OA and exhibited superiority in balancing user's accuracy. For example, the proposed approach was typically 2%-10% more accurate than the compared methods in terms of OA and it generally yielded the most balanced classification.

Citation

Lv, Z., Li, G., Jin, Z., Benediktsson, J. A., & Foody, G. M. (2021). Iterative Training Sample Expansion to Increase and Balance the Accuracy of Land Classification from VHR Imagery. IEEE Transactions on Geoscience and Remote Sensing, 59(1), 139-150. https://doi.org/10.1109/TGRS.2020.2996064

• Journal Article Type: Article
• Acceptance Date: May 17, 2020
• Online Publication Date: Jun 2, 2020
• Publication Date: Jan 1, 2021
• Deposit Date: Jun 6, 2020
• Publicly Available Date: Jan 6, 2021
• Journal: IEEE Transactions on Geoscience and Remote Sensing
• Print ISSN: 0196-2892
• Electronic ISSN: 1558-0644
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 59
• Issue: 1
• Pages: 139-150
• DOI: https://doi.org/10.1109/TGRS.2020.2996064
• Keywords: Electrical and Electronic Engineering; General Earth and Planetary Sciences
• Public URL: https://nottingham-repository.worktribe.com/output/4597238
• Publisher URL: https://ieeexplore.ieee.org/document/9106828
• Additional Information: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.