High-Rankness Regularized Semi-Supervised Deep Metric Learning for Remote Sensing Imagery
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Other documents of the author: kang, jian; Fernandez-Beltran, Ruben; Ye, Zhen; Tong, Xiaohua; Ghamisi, Pedram; Plaza, Antonio
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comunitat-uji-handle2:10234/8013
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Title
High-Rankness Regularized Semi-Supervised Deep Metric Learning for Remote Sensing ImageryAuthor (s)
Date
2020Publisher
MDPIISSN
2072-4292Bibliographic citation
KANG, Jian, et al. High-Rankness Regularized Semi-Supervised Deep Metric Learning for Remote Sensing Imagery. Remote Sensing, 2020, vol. 12, núm. 16, p. 2603Type
info:eu-repo/semantics/articlePublisher version
https://www.mdpi.com/2072-4292/12/16/2603Version
info:eu-repo/semantics/publishedVersionAbstract
Deep metric learning has recently received special attention in the field of remote sensing (RS) scene characterization, owing to its prominent capabilities for modeling distances among RS images based on their semantic ... [+]
Deep metric learning has recently received special attention in the field of remote sensing (RS) scene characterization, owing to its prominent capabilities for modeling distances among RS images based on their semantic information. Most of the existing deep metric learning methods exploit pairwise and triplet losses to learn the feature embeddings with the preservation of semantic-similarity, which requires the construction of image pairs and triplets based on the supervised information (e.g., class labels). However, generating such semantic annotations becomes a completely unaffordable task in large-scale RS archives, which may eventually constrain the availability of sufficient training data for this kind of models. To address this issue, we reformulate the deep metric learning scheme in a semi-supervised manner to effectively characterize RS scenes. Specifically, we aim at learning metric spaces by utilizing the supervised information from a small number of labeled RS images and exploring the potential decision boundaries for massive sets of unlabeled aerial scenes. In order to reach this goal, a joint loss function, composed of a normalized softmax loss with margin and a high-rankness regularization term, is proposed, as well as its corresponding optimization algorithm. The conducted experiments (including different state-of-the-art methods and two benchmark RS archives) validate the effectiveness of the proposed approach for RS image classification, clustering and retrieval tasks. The codes of this paper are publicly available. [-]
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Remote Sensing, 2020, vol. 12, núm. 16, p. 2603Investigation project
This work was supported in part by the National Key Research and Development Project of Chinaunder Grant 2018YFB0505400 and Grant 2017YFB0502700, in part by the National Natural Science Foundation ofChina under Grant 41631178, in part by the Spanish Ministry of Economy under Grant RTI2018-098651-B-C54, n part by FEDER-Junta de Extremadura under Grant GR18060, and in part by the European Union under theH2020 EOXPOSURE Project under Grant 734541.Rights
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