FUSING DIFFERENT LEVELS OF DEEP FEATURES BY DEEP STACKED NEURAL NETWORK FOR HYPERSPECTRAL IMAGES

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

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Detail(s)

Original languageEnglish
Title of host publication2017 IEEE International Geoscience & Remote Sensing Symposium
Subtitle of host publicationInternational Cooperation for Global Awareness, IGARSS 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages759-762
ISBN (Print)9781509049516
Publication statusPublished - Jul 2017

Conference

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

Abstract

Deep learning techniques have been demonstrated to be a powerful tool to learn features of images automatically. In this paper, a novel deep learning structure, i.e., deep stacked neural network (DSNN), is constructed to extract different levels of deep features of hyperspectral images. Specifically, convolutional neural network (CNN) is used as basic units in the proposed DSNN for feature extraction of hyperspectral images. Then, different levels of deep features are concatenated to form a novel fused feature for classification with a typical classifier, e.g., SVM. Experimental results on two benchmark hyperspectral datasets show that the fusion of features extracted in DSNN can produce higher classification accuracy than state-of-the-art deep learning based methods, indicating its effectiveness in feature learning.

Research Area(s)

  • convolutional neural network, deep learning, deep stacked neural network, feature fusion, feature learning

Citation Format(s)

FUSING DIFFERENT LEVELS OF DEEP FEATURES BY DEEP STACKED NEURAL NETWORK FOR HYPERSPECTRAL IMAGES. / Mei, Shaohui; Chen, Yanfu; Ji, Jingyu; Hou, Junhui; Du, Qian.

2017 IEEE International Geoscience & Remote Sensing Symposium: International Cooperation for Global Awareness, IGARSS 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. p. 759-762 8127063.

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