Integrating spectral and spatial information into deep convolutional Neural Networks for hyperspectral classification

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

33 Scopus Citations
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Author(s)

  • Shaohui Mei
  • Jingyu Ji
  • Qianqian Bi
  • Qian Du
  • Wei Li

Detail(s)

Original languageEnglish
Title of host publicationInternational Geoscience and Remote Sensing Symposium (IGARSS)
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5067-5070
Volume2016-November
ISBN (Print)9781509033324
Publication statusPublished - 1 Nov 2016
Externally publishedYes

Publication series

Name
Volume2016-November

Conference

Title36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016
PlaceChina
CityBeijing
Period10 - 15 July 2016

Abstract

Deep convolutional neural networks (CNNs) have brought in achievements in image classification and tar- get detection. In this paper, we propose a novel five-layer CNN for hyperspectral classification by encountering recent achievement in deep learning area, such as batch normaliza- tion, dropout, Parametric Rectified Linear Unit (PReLu) acti- vation function. By taking advantage of the specific charac- teristics of hyperspectral images, spatial context and spectral information are elegantly integrated into the framework. Ex- perimental results demonstrate that our proposed CNN out- performs the state-of-the-art methods.

Research Area(s)

  • Convolutional Neural Networks, deep learning, hyperspectral classification

Citation Format(s)

Integrating spectral and spatial information into deep convolutional Neural Networks for hyperspectral classification. / Mei, Shaohui; Ji, Jingyu; Bi, Qianqian; Hou, Junhui; Du, Qian; Li, Wei.

International Geoscience and Remote Sensing Symposium (IGARSS). Vol. 2016-November Institute of Electrical and Electronics Engineers Inc., 2016. p. 5067-5070 7730321.

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review