Learning sensor-specific features for hyperspectral images via 3-dimensional convolutional autoencoder

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

12 Scopus Citations
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Author(s)

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Detail(s)

Original languageEnglish
Title of host publication2017 IEEE International Geoscience & Remote Sensing Symposium
Subtitle of host publicationProceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1820-1823
ISBN (Electronic)9781509049516
ISBN (Print)9781509049523
Publication statusPublished - Jul 2017

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)
Volume2017
ISSN (Electronic)2153-7003

Conference

Title37th IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2017)
PlaceUnited States
CityFort Worth
Period23 - 28 July 2017

Abstract

Deep learning techniques have brought in revolutionary achievements for feature learning of images. In this paper, a novel structure of 3-Dimensional Convolutional AutoEncoder (3D-CAE) is proposed for hyperspectral spatial-spectral feature learning, in which the spatial context is considered by constructing a 3-Dimensional input using pixels in a spatial neighborhood. All the parameters involved in the 3D-CAE are trained without the need of labeled training samples such that feature learning is conducted in an unsupervised fashion. Such unsupervised spatial-spectral feature extraction is also extended to different images from the same sensor to learn sensor-specific features. As a result, spatial-spectral features of hyperspectral images are extracted for a specific sensor under an unsupervised manner. Experimental results on several benchmark hyperspectral datasets have demonstrated that our proposed 3D-CAE are very effective in extracting sensor-specific spatial-spectral features and outperform several state-of-the-art deep learning neural networks in classification application.

Research Area(s)

  • convolutional autoencoder, deep learning, hyperspectral classification

Citation Format(s)

Learning sensor-specific features for hyperspectral images via 3-dimensional convolutional autoencoder. / Ji, Jingyu; Mei, Shaohui; Hou, Junhui et al.
2017 IEEE International Geoscience & Remote Sensing Symposium: Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. p. 1820-1823 8127329 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2017).

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review