期刊
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
卷 158, 期 -, 页码 35-49出版社
ELSEVIER
DOI: 10.1016/j.isprsjprs.2019.09.008
关键词
Dimensionality reduction; Graph learning; Hyperspectral image; Iterative; Label propagation; Multitask regression; Remote sensing; Semi-supervised
类别
资金
- NSF
- European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program [ERC-2016-StG-714087]
- Helmholtz Association [VH-NG-1018]
- German Research Foundation (DFG) [ZH 498/7-2]
- Japan Society for the Promotion of Science (JSPS) KAKENHI [15K20955]
- Grants-in-Aid for Scientific Research [15K20955] Funding Source: KAKEN
Hyperspectral dimensionality reduction (HDR), an important preprocessing step prior to high-level data analysis, has been garnering growing attention in the remote sensing community. Although a variety of methods, both unsupervised and supervised models, have been proposed for this task, yet the discriminative ability in feature representation still remains limited due to the lack of a powerful tool that effectively exploits the labeled and unlabeled data in the HDR process. A semi-supervised HDR approach, called iterative multitask regression (IMR), is proposed in this paper to address this need. IMR aims at learning a low-dimensional subspace by jointly considering the labeled and unlabeled data, and also bridging the learned subspace with two regression tasks: labels and pseudo-labels initialized by a given classifier. More significantly, IMR dynamically propagates the labels on a learnable graph and progressively refines pseudo-labels, yielding a well-conditioned feedback system. Experiments conducted on three widely-used hyperspectral image datasets demonstrate that the dimension reduced features learned by the proposed IMR framework with respect to classification or recognition accuracy are superior to those of related state-of-the-art HDR approaches.
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