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dc.contributor.authorZhong, Zilong
dc.contributor.authorLi, Jonathan
dc.contributor.authorMa, Lingfei
dc.contributor.authorJiang, Han
dc.contributor.authorZhao, He 17:31:46 (GMT) 17:31:46 (GMT)
dc.descriptionCopyright 2017 IEEE. Published in the IEEE 2017 International Geoscience & Remote Sensing Symposium (IGARSS 2017), scheduled for July 23-28, 2017 in Fort Worth, Texas, USA. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works, must be obtained from the IEEE. Contact: Manager, Copyrights and Permissions / IEEE Service Center / 445 Hoes Lane / P.O. Box 1331 / Piscataway, NJ 08855-1331, USA. Telephone: + Intl. 908-562-3966.en
dc.description.abstractDeep neural networks can learn deep feature representation for hyperspectral image (HSI) interpretation and achieve high classification accuracy in different datasets. However, counterintuitively, the classification performance of deep learning models degrades as their depth increases. Therefore, we add identity mappings to convolutional neural networks for every two convolutional layers to build deep residual networks (ResNets). To study the influence of deep learning model size on HSI classification accuracy, this paper applied ResNets and CNNs with different depth and width using two challenging datasets. Moreover, we tested the effectiveness of batch normalization as a regularization method with different model settings. The experimental results demonstrate that ResNets mitigate the declining-accuracy effect and achieved promising classification performance with 10% and 5% training sample percentages for the University of Pavia and Indian Pines datasets, respectively. In addition, t-Distributed Stochastic Neighbor Embedding (t-SNE) provides a direct view of the extracted features through dimensionality reduction.en
dc.publisherInstitute of Electrical and Electronics Engineersen
dc.subjectDeep residual networksen
dc.subjectDeep learningen
dc.subjectHyperspectral image classificationen
dc.titleDeep Residual Networks for Hyperspectral Image Classificationen
dc.typeConference Paperen
dcterms.bibliographicCitationZhong, Z., Li, J., Ma, L., Jiang, H., & Zhao, H. (2017). IGARSS 2017 | 2017 IEEE International Geoscience and Remote Sensing Symposium | 23-28 July 2017 | Fort Worth, Texas, USA. Presented at the 2017 IEEE International Geoscience and Remote Sensing Symposium, Fort Worth, Texas, USA: IEEE. Retrieved from
uws.contributor.affiliation1Faculty of Environmenten
uws.contributor.affiliation2Geography and Environmental Managementen

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