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http://hdl.handle.net/10397/93522
Title: | Deep clustering with intraclass distance constraint for hyperspectral images | Authors: | Sun, J Wang, W Wei, X Fang, L Tang, X Xu, Y Yu, H Yao, W |
Issue Date: | May-2021 | Source: | IEEE transactions on geoscience and remote sensing, May 2021, v. 59, no. 5, p. 4135-4149 | Abstract: | The high dimensionality of hyperspectral images often results in the degradation of clustering performance. Due to the powerful ability of potential feature extraction and nonlinear representation, deep clustering algorithms have become a hot topic in hyperspectral remote sensing. Different tasks often need different features. However, the current deep clustering algorithms generally separate feature extraction from clustering, which results in the extracted features that are not constrained by clustering tasks. Therefore, the features extracted by these algorithms may not be suitable for clustering. To address this issue, we adopt intraclass distance as a constraint condition and proposed an intraclass distance constrained deep clustering algorithm for hyperspectral images. The proposed algorithm propagates the clustering error back to the feature mapping process of the autoencoder network, so as to realize the constraint of clustering objective on feature extraction and make the extracted features more suitable for clustering tasks. In addition, the proposed algorithm simultaneously completes network optimization and clustering, which is more efficient. Experimental results demonstrate the intense competitiveness of the proposed algorithm in comparison with state-of-the-art clustering methods for hyperspectral images. | Keywords: | Deep learning Hyperspectral images clustering Intraclass distance constraint Low-dimensional (LD) representation Remote sensing |
Publisher: | Institute of Electrical and Electronics Engineers | Journal: | IEEE transactions on geoscience and remote sensing | ISSN: | 0196-2892 | EISSN: | 1558-0644 | DOI: | 10.1109/TGRS.2020.3019313 | Rights: | © 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. The following publication Sun, J., Wang, W., Wei, X., Fang, L., Tang, X., Xu, Y., ... & Yao, W. (2020). Deep clustering with intraclass distance constraint for hyperspectral images. IEEE Transactions on Geoscience and Remote Sensing, 59(5), 4135-4149 is available at https://doi.org/10.1109/TGRS.2020.3019313 |
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Yao_Deep_Clustering_Intraclass.pdf | Pre-Published version | 7.95 MB | Adobe PDF | View/Open |
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